{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-23T03:01:14.158755Z",
     "start_time": "2025-05-23T03:01:14.154463Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from matplotlib import font_manager\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]  # 使用黑体字体\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False"
   ],
   "id": "e6b9916ed40d2a04",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-23T03:01:14.178398Z",
     "start_time": "2025-05-23T03:01:14.174963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def unpickle(file):\n",
    "    import pickle\n",
    "\n",
    "    with open(file, \"rb\") as fo:\n",
    "        dict = pickle.load(fo, encoding=\"bytes\")\n",
    "    return dict"
   ],
   "id": "1472876bca0e88f7",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-23T03:28:18.572447Z",
     "start_time": "2025-05-23T03:28:18.542907Z"
    }
   },
   "cell_type": "code",
   "source": [
    "batch1 = unpickle(\"./data/cifar-10-batches-py/data_batch_1\")\n",
    "print(\"Labels:\")\n",
    "print(batch1[b\"labels\"])\n",
    "print(\"Data:\")\n",
    "print(batch1[b\"data\"])"
   ],
   "id": "a891eef2fa7fdf9f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels:\n",
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1, 2, 9, 2, 2, 1, 6, 3, 9, 1, 1, 5]\n",
      "Data:\n",
      "[[ 59  43  50 ... 140  84  72]\n",
      " [154 126 105 ... 139 142 144]\n",
      " [255 253 253 ...  83  83  84]\n",
      " ...\n",
      " [ 71  60  74 ...  68  69  68]\n",
      " [250 254 211 ... 215 255 254]\n",
      " [ 62  61  60 ... 130 130 131]]\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "使用逻辑回归",
   "id": "619c32b4942bf996"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-23T03:28:56.508410Z",
     "start_time": "2025-05-23T03:28:56.393778Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 准备数据进行逻辑回归训练和评估\n",
    "# CIFAR-10数据集包含10个类别，每个样本是32x32x3的彩色图像，展平后为3072维特征\n",
    "\n",
    "# 提取训练数据和标签\n",
    "batches = []\n",
    "for i in range(1, 6):\n",
    "    batch = unpickle(f\"./data/cifar-10-batches-py/data_batch_{i}\")\n",
    "    batches.append(batch)\n",
    "data = np.concatenate([batch[b\"data\"] for batch in batches], axis=0)\n",
    "labels = np.concatenate([batch[b\"labels\"] for batch in batches], axis=0)\n",
    "# 将数据分为训练集和测试集\n",
    "# 划分比例：80%训练集，20%测试集\n",
    "data_len = data.shape[0]\n",
    "train_data_len = int(data_len * 0.8)\n",
    "test_data_len = data_len - train_data_len\n",
    "# 开发时先使用较小数据\n",
    "scale_factor = 0.01\n",
    "\n",
    "data = data[: int(data_len * scale_factor)]\n",
    "labels = labels[: int(data_len * scale_factor)]\n",
    "data_len = data.shape[0]\n",
    "train_data_len = int(data_len * 0.8)\n",
    "test_data_len = data_len - train_data_len\n",
    "\n",
    "print(f\"train_data_len: {train_data_len}, test_data_len: {test_data_len}\")"
   ],
   "id": "631730c37520251",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_data_len: 400, test_data_len: 100\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-23T03:29:00.538542Z",
     "start_time": "2025-05-23T03:29:00.529965Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X_train = data[:train_data_len]\n",
    "y_train = labels[:train_data_len]\n",
    "X_test = data[train_data_len:]\n",
    "y_test = labels[train_data_len:]\n",
    "\n",
    "# 数据预处理：将像素值标准化到0-1范围\n",
    "X_train_scaled = X_train.astype(\"float32\") / 255.0\n",
    "X_test_scaled = X_test.astype(\"float32\") / 255.0\n",
    "print(f\"X_train_scaled: {X_train_scaled.shape}, y_train: {y_train.shape}\")\n",
    "print(f\"X_test_scaled: {X_test_scaled.shape}, y_test: {y_test.shape}\")"
   ],
   "id": "55816f1b3cc40d60",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_scaled: (400, 3072), y_train: (400,)\n",
      "X_test_scaled: (100, 3072), y_test: (100,)\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-23T03:29:24.740871Z",
     "start_time": "2025-05-23T03:29:02.497977Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 创建不同的分类器及其参数\n",
    "classifiers = {\n",
    "    \"Logistic Regression\": {\n",
    "        \"model\": LogisticRegression(random_state=42),\n",
    "        \"params\": {\"C\": [0.1, 1], \"max_iter\": [100]},\n",
    "    },\n",
    "    \"SVM\": {\n",
    "        \"model\": SVC(random_state=42),\n",
    "        \"params\": {\"C\": [0.1, 1, 10], \"kernel\": [\"linear\", \"rbf\"]},\n",
    "    },\n",
    "    \"Decision Tree\": {\n",
    "        \"model\": DecisionTreeClassifier(random_state=42),\n",
    "        \"params\": {\"max_depth\": [None, 10, 20, 30], \"min_samples_split\": [2, 5, 10]},\n",
    "    },\n",
    "    \"Random Forest\": {\n",
    "        \"model\": RandomForestClassifier(random_state=42),\n",
    "        \"params\": {\"n_estimators\": [10, 50, 100], \"max_depth\": [None, 10, 20]},\n",
    "    },\n",
    "    \"KNN\": {\n",
    "        \"model\": KNeighborsClassifier(),\n",
    "        \"params\": {\"n_neighbors\": [3, 5, 7, 10], \"weights\": [\"uniform\", \"distance\"]},\n",
    "    },\n",
    "    \"Naive Bayes\": {\"model\": GaussianNB(), \"params\": {}},\n",
    "}\n",
    "\n",
    "# 存储每个分类器的最佳模型和分数\n",
    "best_models = {}\n",
    "best_scores = {}\n",
    "cv_scores = {}\n",
    "training_times = {}\n",
    "\n",
    "# 循环每个分类器，进行训练和评估\n",
    "for name, clf in classifiers.items():\n",
    "    print(f\"\\n正在训练 {name}...\")\n",
    "    start_time = time.time()\n",
    "\n",
    "    # 使用GridSearchCV找到最佳参数\n",
    "    if clf[\"params\"]:\n",
    "        grid_search = GridSearchCV(\n",
    "            clf[\"model\"], clf[\"params\"], cv=3, n_jobs=-1, scoring=\"accuracy\"\n",
    "        )\n",
    "        grid_search.fit(X_train_scaled, y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "        best_params = grid_search.best_params_\n",
    "        print(f\"最佳参数: {best_params}\")\n",
    "    else:\n",
    "        best_model = clf[\"model\"]\n",
    "        best_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "    # 记录训练时间\n",
    "    training_times[name] = time.time() - start_time\n",
    "\n",
    "    # 计算训练集和测试集的准确率\n",
    "    train_pred = best_model.predict(X_train_scaled)\n",
    "    train_accuracy = accuracy_score(y_train, train_pred)\n",
    "\n",
    "    test_pred = best_model.predict(X_test_scaled)\n",
    "    test_accuracy = accuracy_score(y_test, test_pred)\n",
    "\n",
    "    # 保存最佳模型和分数\n",
    "    best_models[name] = best_model\n",
    "    best_scores[name] = test_accuracy\n",
    "\n",
    "    # 使用交叉验证评估模型\n",
    "    cv_score = cross_val_score(\n",
    "        best_model, X_train_scaled, y_train, cv=5, scoring=\"accuracy\"\n",
    "    )\n",
    "    cv_scores[name] = np.mean(cv_score)\n",
    "\n",
    "    print(f\"训练集准确率: {train_accuracy:.4f}\")\n",
    "    print(f\"测试集准确率: {test_accuracy:.4f}\")\n",
    "    print(f\"交叉验证准确率: {np.mean(cv_score):.4f} ± {np.std(cv_score):.4f}\")\n",
    "    print(f\"训练时间: {training_times[name]:.2f} 秒\")\n",
    "\n",
    "    # 计算并绘制混淆矩阵\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    cm = confusion_matrix(y_test, test_pred)\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "    plt.title(f\"{name} 混淆矩阵\")\n",
    "    plt.xlabel(\"预测标签\")\n",
    "    plt.ylabel(\"真实标签\")\n",
    "    plt.show()\n",
    "\n",
    "# 找出性能最好的模型\n",
    "best_classifier = max(best_scores, key=best_scores.get)\n",
    "best_accuracy = best_scores[best_classifier]\n",
    "final_model = best_models[best_classifier]\n",
    "\n",
    "print(f\"\\n性能最好的模型: {best_classifier}\")\n",
    "print(f\"测试集准确率: {best_accuracy:.4f}\")\n",
    "\n",
    "# 可视化不同模型的性能比较\n",
    "plt.figure(figsize=(12, 6))\n",
    "names = list(best_scores.keys())\n",
    "scores = [best_scores[name] for name in names]\n",
    "cv = [cv_scores[name] for name in names]\n",
    "times = [training_times[name] for name in names]\n",
    "\n",
    "# 准确率对比\n",
    "plt.subplot(1, 2, 1)\n",
    "bars = plt.bar(names, scores, color=\"skyblue\")\n",
    "plt.ylim(0, 1)\n",
    "plt.ylabel(\"测试集准确率\")\n",
    "plt.title(\"不同分类器的准确率比较\")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(\n",
    "        bar.get_x() + bar.get_width() / 2.0,\n",
    "        height + 0.01,\n",
    "        f\"{height:.4f}\",\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "    )\n",
    "\n",
    "# 训练时间对比\n",
    "plt.subplot(1, 2, 2)\n",
    "bars = plt.bar(names, times, color=\"salmon\")\n",
    "plt.ylabel(\"训练时间 (秒)\")\n",
    "plt.title(\"不同分类器的训练时间比较\")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(\n",
    "        bar.get_x() + bar.get_width() / 2.0,\n",
    "        height + 0.1,\n",
    "        f\"{height:.2f}s\",\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "    )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "f3185554b06e4a09",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 Logistic Regression...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数: {'C': 0.1, 'max_iter': 100}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "D:\\an\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "D:\\an\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "D:\\an\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "D:\\an\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集准确率: 0.9525\n",
      "测试集准确率: 0.3000\n",
      "交叉验证准确率: 0.2550 ± 0.0400\n",
      "训练时间: 4.11 秒\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 SVM...\n",
      "最佳参数: {'C': 10, 'kernel': 'rbf'}\n",
      "训练集准确率: 1.0000\n",
      "测试集准确率: 0.3400\n",
      "交叉验证准确率: 0.2925 ± 0.0510\n",
      "训练时间: 1.76 秒\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 Decision Tree...\n",
      "最佳参数: {'max_depth': 10, 'min_samples_split': 5}\n",
      "训练集准确率: 0.8600\n",
      "测试集准确率: 0.2100\n",
      "交叉验证准确率: 0.1750 ± 0.0326\n",
      "训练时间: 2.24 秒\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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iRXrooYfcl0yNiYlxJwPt2rVzv/7XX3+t9evXZztmenq6+vbte0ksF/s0IiJCr776qh577DEtXLjwsnMo/tfFYWOVKlXS2rVr3XH27NlTvr6+8vHxUUJCgrp27ar3339fdevWVWZmplJSUhQSEpKr9woArIqkAcBVGT16tPz9/VWnTh2988477jkAktSqVSvNnDlT7777rubNm6eKFStq8uTJuuOOOyRJ5cqV04IFCzR58mQNHTpUpUuX1oMPPnjZq+vk5JFHHlFycrKmT5+uP//8U+XKldOLL76o5s2b5/o13nnnHU2dOlUTJkxQRkaGbrvtNo0dO9b06sK/lSpVSh999JFee+01DR48WMHBwerdu7eeeuop9zZvvfWWpkyZovDwcNlsNrVp08b937nx6quvyul0um/GtnPnTjmdToWHhysqKkrdunXTgAEDlJWVJafTqaCgIP3444+aM2eO+/KwCxYsUKlSpS5787r//Oc/l53T8L8WLFigTz75RE8//bQqV66s6dOnq3fv3vrss8+UkpJy2fs0pKamKj09XdWrV7/s/IZ/32BP+qdyFBwcnOukCgAg2bLy+oLjAIACZcGCBfL391evXr0kSQsXLtTs2bP18MMPq1evXipRooTOnTun7t27KyEhQcWKFVNCQoIGDRqkUaNG6bffftOdd96pzp0766233sr22i+99JISExPdSUNWVpZeffVVrVixQr1791Z4eLikC8PNxo8fr27dumn69OnuhCc2NlZlypRRu3bt3Jdi/beLCcOSJUv0ww8/6PHHH9fhw4dzbOtff/2l1q1ba8GCBR4lmABgdVQaAMDiBgwYkG393nvvVbdu3bJdiapEiRLavHnzZfevUqWKli1bplOnTl3yXFxcXLbLotpsNhUrVky1a9fOdsWnu+++W06nUwMGDMhWIbk4bGjLli1XbMett95qmDBIFy4D++//BwDkDpUGAAAAAIa4ehIAAAAAQyQNAAAAQAH0yCOP6Isvvrjidjt27NCdd96pFi1aaP78+Vd1LJIGAAAAoIBZtWpVruZ7xcXFaejQoeratasWL16s1atXa9u2bR4fj6QBAAAAKEDOnTunqVOnqmrVqlfcdtWqVSpTpoyGDRumG2+8UU888YSWLVvm8TG5ehIAAABgIpfLJZfLle0xX19f+fr6Xnb7qVOnqnPnzkpNTb3iax8+fFgtW7Z0X5muQYMGmjlzpscxFqqkwb/xk2aHYIoNS18xOwR4UeMbS5gdArxo1OqDZodgihnd65gdginqPLvG7BBM0aVdNbNDMMXaTcfMDsEUx2d1NTuEHJn1W3Law2GaPXt2tseefPJJDR8+/JJtt23bpp9++klffvmlXnnlyr8BnU6nqlev7l4PCgpSbGysxzEWqqQBAAAAKGgGDx6sQYMGZXvsclWG1NRUvfTSS5owYYKCgoJy9dp2uz3ba/n5+V3VvWpIGgAAAABJspkz3ddoKNK/vfvuu7rpppvUvn37XL92cHCw4uLi3OvJyclyOBwex0jSAAAAABQAq1evVnx8vJo1aybpwt3tv/rqK+3Zs0cTJky47D7169fXmjX/DH08ePCgQkJCPD42SQMAAABQAHz++edKT093r0+bNk0NGzbU3XffLafTKT8/v0uqCB07dtTEiRO1bds2NWvWTPPmzVPbtm09PjZJAwAAACBJ/3eFoetVuXLlsq0HBASoZMmSKlWqlDp27KixY8eqc+fO2bYpVaqUnnvuOT366KMKCgpSQECAXn31VY+PTdIAAAAAFECvvfaa+783bNiQ43b9+/dX27ZtFRUVpZtvvjnXk6j/jaQBAAAAkEybCO0NVapUUZUqVa56/8L7zgAAAADIEyQNAAAAAAwxPAkAAACQrvuJ0Gai0gAAAADAEJUGAAAAQCrUE6GvFe8MAAAAAENUGgAAAACJOQ0GqDQAAAAAMETSAAAAAMAQw5MAAAAAiYnQBnhnAAAAABii0gAAAABITIQ2QKUBAAAAgCGSBgAAAACGGJ4EAAAASEyENsA7AwAAAMAQSQMAABZXIsChJjeWVMlAh9mhwAvobwM2mzlLAUDSkEdWzn5CA7q3MDsMr3ImJmj0wz31Z+wps0PxKqu2G9bRIDRIL99eXW/dVVvP3nqjQor5mh0S8lG3xqH6flwHTexVT1tf7KRujUPNDskrrHqeW7W/ce1IGvLA/Xc20+1t6podhlclJZzTrJdH6mzsabND8Sqrtvvo0SPq16eX2rZqrpnTpyorK8vskLzCiu0uHejQgCbltXL/GY37+qjizqepv0V+VFixv4sVLaIJ99RTn7d/VLcZWzRu6V4936222WHlO6ue51btb4/YfMxZCoCCEeV1rGTxAE0ZeY8OH48xOxSvem/qeLVod5vZYXidFdvtcrk0YtgQ1alXTwsXL9exqCitjPjC7LDynVXbXa6Yn1btP6Nd0UlKSs3QpuPxqlyiqNlh5Tur9ndQ0SKatOKAjsQ4JUkHoxNVPKDwD1mx6nlu1f5G3rgukoakpCSdOXNG8fHxBe4vO6+NvEerNkZqx94TZofiVQOHh+v2u+43Owyvs2K7t2zeJGeSU6PHhKtS5coa/tRIrVi+zOyw8p1V270vxqktJ86510OCfHU2Oc28gLzEqv19+lyKVu66MNSyiI9Nj3Wopm/2FP4/gln1PLdqfyNvmHbJ1RUrVmjp0qWKioqS3W6Xr6+vkpOTlZaWplatWmn06NGqXr26WeHlSrtmNdXh5lpqeu9kzRjT2+xwvKpsaAWzQzCFFdt95PAhNWjYUP7+/pKkWmFhOhYVZXJU+c+q7f43u03qXPMGbfg1zuxQ8p3V+7tO+WL6/ImWSsvIVKcpP5gdjldZ6Ty/yMr9fUUFZFKyGUypNLz++uv65ptv9OKLL2r79u368ccf9f3332vnzp368ssvVaZMGQ0YMEDx8fFmhJcrfr5FNHt8X42YvFhJySlmhwPkG6fTqQoVKrrXbTab7HYfJSYkmBhV/rNqu/+te92ySk3P1JYT1+93cV6xen8fPJWk/u9t15EYp17v29DscLzKSuf5RVbub1w9U5KGZcuWady4capd+9LJNxUrVtTEiRPl4+OjX375xYTocif8sTv18/7f9PWW/WaHAuQru90uh2/2q4r4+vnp75TCnSxbtd0X1S4TqLZVS2j+f6OVWbBGjV4Vq/e3JB2ITtSzCyN1200hKu5vjXu/Wu08/zcr9neuMBE6R6ZEWalSJc2bN0+pqamXfX7lypVKTk7WTTfd5OXIcu++O5uqW/sGOr1pmk5vmqb77mymN8Pv0xvhfcwODchTwcHBio/PXrY/n5wsh6NwT56zarsl6YYAhwY2L6/Fu2MUk+QyOxyvsGp/t6pxg8K7//MHvPT/++VcwKYXXhUrnudW7m9cO1NSy8mTJ2vo0KFau3atGjVqpIoVK8rX11dxcXHavXu3nE6npk+frrJly5oRXq50fvgN2e3/5FyvjbxbO/ac0Kert5kYFZD36t1UP9uE0Ojok3K5XAoODjYxqvxn1XY7fGwa2qqSIk8lac/pJPnZL4zvTc0o3L8qrNrfUWecmvNIU504m6zvD/6pUV3CtPnwn0pKSTc7tHxl1fPcqv2NvGFK0lCrVi199dVX2rRpkw4dOqTk5GTZ7XbVqFFDd999t5o3by673W5GaLkWfeZctnXn+VSdPefUX+eSzQkIyCdNmzVXkjNJq1dGqPtdPTVv7vtq0bL1df8ZvVZWbXedkECFFvdTaHE/ta1a0v34C9/8qrjzhffqMlbt7zOJqXryo10a37OuwnvU0eZDZzXys0izw8p3Vj3PrdrfHikgQ4XMYMsqaNc4NeDf+EmzQzDFhqWvmB0CvKjxjSW8fswN361X+JhRCggMVGZGhj78eIFq1Kjp9Ti87Xpo96jVB716vOvFjO51vH7M66G/6zy7xqvHu150aVfN7BBMsXbTMbNDMMXxWV3NDiFH/rdONOW4f//woinH9QQzXwBcUcdOnbVq7bc6sH+vGjZqolKlSpkdkldYtd1WRX8DkA+XXM0JSQOAXAkJCVFISIjZYXidVdttVfQ3AFweSQMAAAAgMafBAO8MAAAAAEMkDQAAAAAMMTwJAAAAkCQbE6FzQqUBAAAAgCEqDQAAAIDERGgDvDMAAAAADJE0AAAAADDE8CQAAABAYiK0ASoNAAAAAAxRaQAAAAAkJkIb4J0BAAAAYIhKAwAAACAxp8EAlQYAAAAAhkgaAAAAABhieBIAAAAgMRHaAO8MAAAAAENUGgAAAACJidAGqDQAAAAAMETSAAAAAMAQw5MAAAAAiYnQBnhnAAAAABii0gAAAABITIQ2QKUBAAAAgKFCVWnYsPQVs0MwxYK9p80OwRQD6oeaHQK8aNTqg2aHYArOc2s5+HpXs0MArI05DTninQEAAABgiKQBAAAAgKFCNTwJAAAAuGoMT8oR7wwAAAAAQ1QaAAAAAIlLrhqg0gAAAADAEEkDAAAAAEMMTwIAAAAkJkIb4J0BAAAACpj4+Hjt2rVLcXFxXjkeSQMAAAAgXZgIbcbioTVr1uj222/XxIkT1aFDB61Zs+aK+wwZMkRhYWHuZeDAgR4dk+FJAAAAQAGRmJioSZMm6bPPPlOtWrUUERGh6dOnq2vXrob77du3T6tXr1a5cuUkSUWKeJYGkDQAAAAABURycrLGjh2rWrVqSZJq166thIQEw31iYmIkyb3P1WB4EgAAACBdmAhtxuKB0NBQ9ejRQ5KUlpamefPm6fbbbzfcZ8+ePcrIyFC7du3UqFEjPfPMM1dMNP4XSQMAAABgIpfLJafTmW1xuVyG+xw6dEht2rTR1q1bNXbsWMNtT5w4oXr16unDDz/U8uXLFR0drZkzZ3oUI0kDAAAAIJk2EXrOnDlq2rRptmXOnDmGoYaFhemjjz5SjRo1FB4ebrjt448/rrlz56pmzZqqXr26Ro8erW+++cajt4Y5DQAAAICJBg8erEGDBmV7zNfX13Afm82munXr6rXXXlOHDh2UkJCg4ODgXB2vePHiio+Pl8vluuJxLqLSAAAAAOjCD3EzFl9fXwUFBWVbcvox/9NPP2nq1KnudbvdLkny8cn5Z/2IESO0e/du9/revXtVpkyZXCcMEkkDAAAAUGBUq1ZNixcv1uLFi3X69GnNmDFDbdq0UbFixeR0OpWWlnbJPrVq1dKUKVMUGRmpjRs36s0331Tfvn09Oi5JAwAAAFBAhISE6M0339THH3+srl276u+//9brr78uSerRo4d++OGHS/YZPHiwqlevrkGDBmny5Mm6//77NXjwYI+Oy5wGAAAAQBeGJxUEt9xyi2655ZZLHt+wYcNlt3c4HJo8ebImT5581cek0gAAAADAEJUGAAAAQJIKRqHBFFQaAAAAABgiaQAAAABgiKQhDzgTEzT64Z76M/aU2aF4TYPQIL18e3W9dVdtPXvrjQoplvvr/BZ0Vuzvo0ePqF+fXmrbqrlmTp+qrKwss0PyCs5zznMroN3WareVv9dyw6z7NBQEJA3XKCnhnGa9PFJnY0+bHYrXlA50aECT8lq5/4zGfX1UcefT1L9xqNlheYUV+9vlcmnEsCGqU6+eFi5ermNRUVoZ8YXZYeU7znPOcyuc57TbWu228vcarh1JwzV6b+p4tWh3m9lheFW5Yn5atf+MdkUnKSk1Q5uOx6tyiaJmh+UVVuzvLZs3yZnk1Ogx4apUubKGPzVSK5YvMzusfMd5znluhfOcdlur3Vb+XsstKg05I2m4RgOHh+v2u+43Owyv2hfj1JYT59zrIUG+Opt86d0HCyMr9veRw4fUoGFD+fv7S5JqhYXpWFSUyVHlP85zznMrnOe021rttvL3Gq4dScM1KhtawewQTGW3SZ1r3qBNx+LNDsUrrNjfTqdTFSpUdK/bbDbZ7T5KTEgwMSrv4jwv/Kx6ntPuC6zS7n+z2vdablFpyBlJA65J97pllZqeqS0n+NIprOx2uxy+2SfK+fr56e+UFJMi8j7O88LPquc57f6HFdr9b3yvwVOm3Nzt1KncXY2jfPny+RwJrkXtMoFqW7WEpv9wQpnWuOiEJQUHB+vXX49me+x8crIcDodJEXkX57k1WPU8p93/sEK7L+J7DVfDlKThgQcecCcOOV3izGaz6eDBg94MCx64IcChgc3La/HuGMUkucwOB/mo3k31s00QjI4+KZfLpeDgYBOj8g7Oc+uw6nlOuy+wSrslvteupKAMFTKDKcOTli1bpoYNG2rs2LE6dOjQZRcShuuXw8emoa0qKfJUkvacTpKf3SY/Ox+ywqpps+ZKciZp9coISdK8ue+rRcvWstvt5gaWzzjPrcWq5zntjpBknXbzvYZrYUqloWTJknrvvfc0cuRIdejQQRUrVrzyTrhu1AkJVGhxP4UW91PbqiXdj7/wza+KO89VGAqbIkWK6MUJkxQ+ZpRmzpimzIwMffjxArPDynec59Zi1fOcdlur3Xyv5QI5VI5sWYXoFog//XrO7BBMsWCvdW7A9G8D6lvzhjSNbyxhynFjY2N1YP9eNWzURKVKlfL68Uettmb1kfPcu8w+z81Cu/le86Z37q5jdgg5Cu73qSnHTfj8AVOO6wlTKg0ACp6QkBCFhISYHQaQr6x6ntNuAFdC0gAAAACIidBGuE8DAAAAAENUGgAAAABRaTBCpQEAAACAISoNAAAAgKg0GKHSAAAAAMAQSQMAAAAAQwxPAgAAAMTwJCNUGgAAAAAYotIAAAAASBKFhhxRaQAAAABgiKQBAAAAgCGGJwEAAABiIrQRKg0AAAAADFFpAAAAAESlwQiVBgAAAACGqDQAAAAAotJghEoDAAAAAEMkDQAAAAAMMTwJAAAAkLgjtAEqDQAAAAAMUWkAAAAAxERoI1QaAAAAABgiaQAAAABgqFANT1qw97TZIZhiRvc6ZodgilGrD5odArxoQP1Qs0MwRb93tpodgik+H9bG7BBM0fjGEmaHAC+y6r/f1zOGJ+WMSgMAAAAAQ4Wq0gAAAABcLSoNOaPSAAAAAMAQlQYAAABAVBqMUGkAAAAAYIikAQAAAIAhhicBAAAAksTopBxRaQAAAABgiEoDAAAAICZCG6HSAAAAAMAQSQMAAAAAQwxPAgAAAMTwJCNUGgAAAAAYotIAAAAAiEqDESoNAAAAAAxRaQAAAAAkbu5mgEoDAAAAAEMkDQAAAAAMMTwJAAAAEBOhjVBpAAAAAGCISgMAAAAgKg1GqDQAAAAAMETSAAAAAMAQw5MAAJcoEeBQtbJBOv6nU/HJaWaHAwBewfCknFFpuEYNQoP08u3V9dZdtfXsrTcqpJiv2SEhH1m5v52JCRr9cE/9GXvK7FC8yort7tY4VN+P66CJvepp64ud1K1xqNkheY0V+xsAcoOk4RqUDnRoQJPyWrn/jMZ9fVRx59PU3yL/uB49ekT9+vRS21bNNXP6VGVlZZkdUr6zcn8nJZzTrJdH6mzsabND8SortrtY0SKacE899Xn7R3WbsUXjlu7V891qmx2WV1ixvyVrfp9LtNtq7c4tm81mynI14uPjtWvXLsXFxeXxu3B5JA3XoFwxP63af0a7opOUlJqhTcfjVblEUbPDyncul0sjhg1RnXr1tHDxch2LitLKiC/MDivfWbW/Jem9qePVot1tZofhdVZsd1DRIpq04oCOxDglSQejE1U8wGFyVN5hxf626vc57bZWuwujNWvW6Pbbb9fEiRPVoUMHrVmz5or77NixQ3feeadatGih+fPne3xMkoZrsC/GqS0nzrnXQ4J8ddYCY3+3bN4kZ5JTo8eEq1Llyhr+1EitWL7M7LDynVX7W5IGDg/X7Xfdb3YYXmfFdp8+l6KVuy4MzSniY9NjHarpmz0xJkflHVbsb6t+n9Nua7W7sElMTNSkSZP02WefKSIiQi+//LKmT59uuE9cXJyGDh2qrl27avHixVq9erW2bdvm0XGv26Th1KmCNZ7UbpM617xBm47Fmx1Kvjty+JAaNGwof39/SVKtsDAdi4oyOSrvslJ/S1LZ0Apmh2AKq7ZbkuqUL6adEzvrlrDSmrjigNnheIUV+9uq3+e021rt9ojNpMUDycnJGjt2rGrVqiVJql27thISEgz3WbVqlcqUKaNhw4bpxhtv1BNPPKFlyzxLGE1JGo4dO6Z+/fqpQYMG6t69uxYuXKiMjAz38+fPn1enTp3MCO2qda9bVqnpmdpyovD/iHQ6napQoaJ73WazyW73UeIVTtjCxEr9DWs6eCpJ/d/briMxTr3et6HZ4SCfWPX7nHZfYJV2FwQul0tOpzPb4nK5LrttaGioevToIUlKS0vTvHnzdPvttxu+/uHDh9WyZUv3/IkGDRrowAHP/iBkStIQHh6usmXL6oMPPlC/fv00f/589e7dW8eOHXNvU5Am5tQuE6i2VUto/n+jlVlwwr5qdrtdDt/sVw3y9fPT3ykpJkXkXVbrb1jXgehEPbswUrfdFKLi/lyhuzCy6vc57f6HFdrtCbMmQs+ZM0dNmzbNtsyZM8cw1kOHDqlNmzbaunWrxo4da7it0+lUxYr/JIxBQUGKjY316L0xJWnYt2+fXnzxRbVo0UJ9+/bVmjVr1KpVK/Xp08c9kaOgXCf3hgCHBjYvr8W7YxSTdPmMsLAJDg5WfHz2mfrnk5PlcBT+yZJW7G9YS6saNyi8+z9XS0r/v8y4AP0dBx6w6vc57f6HFdpdEAwePFg///xztmXw4MGG+4SFhemjjz5SjRo1FB4ebrit3W6X778SRj8/P6V4mCyakjSEhoZq586d7nWHw6ExY8bozTff1KuvvqrXXnvNjLA85vCxaWirSoo8laQ9p5PkZ7fJz14wkp1rUe+m+tobGelej44+KZfLpeDgYBOjyn9W7W9YS9QZp/q2rqy+rSoptERRPdettjYf/lNJKelmh4Z8YNXvc9p9gVXa7QmzKg2+vr4KCgrKtvj6Gt8LymazqW7dunrttdf03XffGc5rCA4OznZp1uSrSBZNSRqef/55vfjii1q8eHG2x9u0aaPFixfrv//9rxlheaxOSKBCi/upbdWSmtmjtnspVcgvT9i0WXMlOZO0emWEJGne3PfVomVr2e12cwPLZ1btb1jLmcRUPfnRLg1qV1XfPNdO/g67Rn4WeeUdUSBZ9fucdkdIsk67C5uffvpJU6dOda9f7D8fn5x/1tevX1+R/0oYDx48qJCQEI+Oa8syafLAH3/8oaioKLVv3/6S51JTU7V582Z17tzZo9cctuJgHkVXsMzoXsfrx9zw3XqFjxmlgMBAZWZk6MOPF6hGjZpejWHUamv294D61rihHC7o985Ws0MwxefD2pgdgika31jC68e8Hr7PzUC7zWt30et4ilT1UV+ZctyoGXfmetvY2Fjdeeedeu6559SuXTu98cYbOnv2rD788EM5nU75+fldUkWIi4tT+/bt9f7776tZs2YaNmyYKlasqBdeeCHXxzUtacgPJA3eFRsbqwP796phoyYqVaqU149P0gArIGmwFjOSBsn873Oz0G5z2n09Jw01RpuTNPw6PfdJgyRt3rxZU6ZMUUxMjNq2basJEyaoVKlS6tixo8aOHXvZP7x/9tlnmjJlioKCghQQEKAlS5aodOnSuT7mddxtuN6FhIR4XNoCAFx/rPp9TrtRUN1yyy265ZZbLnl8w4YNOe7Tv39/tW3bVlFRUbr55psVFBTk0TFJGgAAAAAVnKt3Xq0qVaqoSpUqV7XvdXtHaAAAAADXB5IGAAAAAIYYngQAAABIKuSjk64JlQYAAAAAhqg0AAAAACr8E6GvBZUGAAAAAIaoNAAAAABiToMRKg0AAAAADJE0AAAAADDE8CQAAABAko8P45NyQqUBAAAAgCEqDQAAAICYCG2ESgMAAAAAQyQNAAAAAAwxPAkAAAAQd4Q2QqUBAAAAgCEqDQAAAICYCG2ESgMAAAAAQ1QaAAAAADGnwQiVBgAAAACGSBoAAAAAGGJ4EgAAACCGJxmh0gAAAADAUKGqNMzoXsfsEOBFazcdMzsEU1j1PP/lxDmzQzDFwde7mh0CvMiq53njG0uYHYIprNrfrWqUMDuEHFFoyBmVBgAAAACGSBoAAAAAGCpUw5MAAACAq8VE6JxRaQAAAABgiEoDAAAAICZCG6HSAAAAAMAQlQYAAABAzGkwQqUBAAAAgCGSBgAAAACGGJ4EAAAAiInQRqg0AAAAADBEpQEAAAAQE6GNUGkAAAAAYIikAQAAAIAhhicBAAAAYiK0ESoNAAAAAAxRaQAAAADERGgjVBoAAAAAGKLSAAAAAIg5DUaoNAAAAAAwRNIAAAAAwBDDkwAAAAAxEdoIlQYAAAAAhqg0AB4qEeBQtbJBOv6nU/HJaWaHAwAA8giFhpxRabhGR48eUb8+vdS2VXPNnD5VWVlZZofkFVZtd7fGofp+XAdN7FVPW1/spG6NQ80OySus2t+S5ExM0OiHe+rP2FNmh+I1Vu1vq7Zb4jynv4ErI2m4Bi6XSyOGDVGdevW0cPFyHYuK0sqIL8wOK99Ztd3FihbRhHvqqc/bP6rbjC0at3Svnu9W2+yw8p1V+1uSkhLOadbLI3U29rTZoXiNVfvbqu2WOM/pbyB3SBquwZbNm+RMcmr0mHBVqlxZw58aqRXLl5kdVr6zaruDihbRpBUHdCTGKUk6GJ2o4gEOk6PKf1btb0l6b+p4tWh3m9lheJVV+9uq7ZY4z+lv/JvNZjNlKQiuq6QhKSlJTqfT7DBy7cjhQ2rQsKH8/f0lSbXCwnQsKsrkqPKfVdt9+lyKVu66UMot4mPTYx2q6Zs9MSZHlf+s2t+SNHB4uG6/636zw/Aqq/a3VdstcZ5L9DeQG6YkDWvXrlXHjh3VrFkzjR07Vk6nU08++aRuvvlm3XzzzXr88ccVHx9vRmgecTqdqlChonvdZrPJbvdRYkKCiVHlP6u2+6I65Ytp58TOuiWstCauOGB2OPnOyv1dNrSC2SF4nVX726rtljjPJfob/7DZzFkKAq8nDfHx8Ro3bpyeeuopffbZZ8rIyNB//vMfJScna+PGjfr+++9VqlQpTZw40duhecxut8vh65vtMV8/P/2dkmJSRN5h1XZfdPBUkvq/t11HYpx6vW9Ds8PJd1bvb6uxan9btd1WRX8DnvN60nDixAlVrVpVd911l8LCwvTKK68oLS1Nzz33nMqVK6eyZcvq6aef1ubNm70dmseCg4MVHx+X7bHzyclyOAr3OHertvvfDkQn6tmFkbrtphAV9y/cVy6mv63Fqv1t1XZbFf2NnDCnIWdeTxqqVq2qkydPKur/xg46HA59/PHHql37n6vQbN68WSEhId4OzWP1bqqvvZGR7vXo6JNyuVwKDg42Mar8Z9V2t6pxg8K7/3OepmdeuDxfYb9Kn1X726qs2t9WbbdV0d+A57yeNJQoUUIvvfSSBgwYoC+//FKSsiUM06ZN02uvvaaxY8d6OzSPNW3WXEnOJK1eGSFJmjf3fbVo2Vp2u93cwPKZVdsddcapvq0rq2+rSgotUVTPdautzYf/VFJKutmh5Sur9rdVWbW/rdpuq6K/Ac+ZMq6ia9euateunZKTky95rl27dho4cKDKli1rQmSeKVKkiF6cMEnhY0Zp5oxpyszI0IcfLzA7rHxn1XafSUzVkx/t0viedRXeo442HzqrkZ9FXnnHAs6q/W1VVu1vq7bbquhv5KSgDBUygy2rEN0C0aw/+MbGxurA/r1q2KiJSpUqZU4QJjC73XWeXeP1Y14PDr7e1ZTjmt3fv5w45/VjXg8a31jClOOa3d9mMbvdnOfeRX+bo1WNEmaHkKN2M7eactxNI9uYclxPFO4ZnF4SEhJSIOZg5DWrttuq6G9rsWp/W7XdVkV/439RaMjZdXVzNwAAAADXH5IGAAAAAIYYngQAAACIidBGqDQAAAAAMETSAAAAAOjCRGgzFk+tX79enTp1Ut26dXXvvfe6b5psZMiQIQoLC3MvAwcO9OiYJA0AAABAAfH7779r7NixGjVqlDZt2qTy5ctr3LhxV9xv3759Wr16tXbu3KmdO3fq3Xff9ei4zGkAAAAACoioqCg988wz6tKliySpb9++evTRRw33iYmJkSTVqlXrqo9L0gAAAACoYEyE7tChQ7b148ePq0qVKob77NmzRxkZGWrXrp0SExPVoUMHTZgwQcHBwbk+LsOTAAAAABO5XC45nc5si8vlytV+8+bNU79+/Qy3O3HihOrVq6cPP/xQy5cvV3R0tGbOnOlRjCQNAAAAgMybCD1nzhw1bdo02zJnzpwrxvvGG28oICBAffr0Mdzu8ccf19y5c1WzZk1Vr15do0eP1jfffOPRe8PwJAAAAMBEgwcP1qBBg7I95uvra7jP1q1btWjRIi1ZskQOh8Oj4xUvXlzx8fFyuVxXPM5FVBoAAAAAST42mymLr6+vgoKCsi1GP+b/+OMPjR49WhMmTFCNGjWu2K4RI0Zo9+7d7vW9e/eqTJkyuU4YJJIGAAAAoMBISUnR4MGD1blzZ3Xq1EnJyclKTk5WVlaWnE6n0tLSLtmnVq1amjJliiIjI7Vx40a9+eab6tu3r0fHZXgSAAAAUEBs2bJFUVFRioqK0pIlS9yPf/fdd3rwwQc1duxYde7cOds+gwcP1qlTpzRo0CDdcMMNuv/++zV48GCPjkvSAAAAAOjq7s7sbZ07d9bhw4cv+9yGDRsu+7jD4dDkyZM1efLkqz4uw5MAAAAAGKLSAAAAAKhg3NzNLFQaAAAAABgiaQAAAABgiOFJAAAAgCQfRifliEoDAAAAAENUGgAAAAAxEdoIlQYAAAAAhqg0AAAAACoYN3czS6FKGkatPmh2CKaY0b2O2SGY4vNhbcwOwRRWPc+tqvGNJcwOwRR1nl1jdgimOPh6V7NDMIVVv9es+u83CiaGJwEAAAAwVKgqDQAAAMDVsonxSTmh0gAAAADAEJUGAAAAQNzczQiVBgAAAACGSBoAAAAAGGJ4EgAAACDuCG2ESgMAAAAAQ1QaAAAAAHFHaCNUGgAAAAAYotIAAAAASPKh1JAjKg0AAAAADJE0AAAAADDE8CQAAABATIQ2QqUBAAAAgCEqDQAAAIC4uZsRKg0AAAAADJE0AAAAADDE8CQAAABATIQ2QqUBAAAAgCEqDQAAAIC4I7QRKg0AAAAADFFpAAAAACRRZ8gZlQYAAP5PiQCHmtxYUiUDHWaHAgDXFSoN16hBaJB61Q9RSX+H/jiXok92nVJsksvssJCPnIkJmvD0Q3puyrsqE1Le7HC8wqrnuVXbbVXdGofqld71dTLuvKqVDdKYRZH68pfTZoeFfMLnG/AMlYZrUDrQoQFNymvl/jMa9/VRxZ1PU//GoWaH5RVHjx5Rvz691LZVc82cPlVZWVlmh+QVSQnnNOvlkToba50fElY9z63absman+9iRYtowj311OftH9VtxhaNW7pXz3erbXZYXmHF/ubzba3+9oTNZjNlKQhIGq5BuWJ+WrX/jHZFJykpNUObjsercomiZoeV71wul0YMG6I69epp4eLlOhYVpZURX5gdlle8N3W8WrS7zewwvMqq57lV223Vz3dQ0SKatOKAjsQ4JUkHoxNVPKDwD1Gyan/z+bZWfyNvkDRcg30xTm05cc69HhLkq7PJaeYF5CVbNm+SM8mp0WPCValyZQ1/aqRWLF9mdlheMXB4uG6/636zw/Aqq57nVm23VT/fp8+laOWuU5KkIj42Pdahmr7ZE2NyVPnPqv3N59ta/e0JH5s5S0FwXSUNEydO1Llz58wO46rYbVLnmjdo07F4s0PJd0cOH1KDhg3l7+8vSaoVFqZjUVEmR+UdZUMrmB2Cqax0nv+bldpt5c+3JNUpX0w7J3bWLWGlNXHFAbPDyXdW72+Jz7fV+htXz+sToSMiInJ8bsWKFapUqZJKliypnj17ei2mvNC9blmlpmdqy4nC/6XjdDpVoUJF97rNZpPd7qPEhAQVDw42MTLkNyud5/9mpXZb/fN98FSS+r+3XWN71NHrfRtqyPyfzQ4pX1m9vyU+31brb1w9rycNX375pbZs2aJq1aqpYcOG2Z5LS0vTL7/8osDAwAKVNNQuE6i2VUto+g8nlGmB+UR2u10OX99sj/n6+envlBS+dAoxq53nF1mt3Xy+pQPRiXp2YaS2vNBRxf2LKPHvdLNDyjdW728+39bq79woKJOSzeD1pGHu3LlatWqVpk2bpqJFi2r06NEKDAyUJK1fv17PP/+8ypcvOJexvCHAoYHNy2vx7hjFWORSbcHBwfr116PZHjufnCyHo/BPGrQqK57nkjXbbdXPd6saN6h9nTKasvqQJCn9/35BFvYLy1i1vyU+3xdZpb9x7UyZ09CjRw+tWbNGKSkp6t69uzZt2mRGGNfM4WPT0FaVFHkqSXtOJ8nPbpOfvfBnqPVuqq+9kZHu9ejok3K5XArmrxSFklXPc6u226qf76gzTvVtXVl9W1VSaImieq5bbW0+/KeSUgpvlUGybn/z+b7AKv3tCZvNnKUgMO3mbsHBwZoyZYq2bduml156SatXr1ZGRoZZ4VyVOiGBCi3up9DifmpbtaT78Re++VVx5wvvVRiaNmuuJGeSVq+MUPe7emre3PfVomVr2e12s0NDPrDqeW7Vdlv1830mMVVPfrRL43vWVXiPOtp86KxGfhZ55R0LOKv2N59va/U38oYt6zq4q4fL5dI777yjL7/8Up9//rlCQkKu6nWGrTiYx5EVDDO61/H6MTd8t17hY0YpIDBQmRkZ+vDjBapRo6ZXY/jlX5fLs5IFe61zYzlY9/Nd59k1Xj3e9eLg6129fszrob9Hrebfb2+5Hvq7qGl/sr6yBz/fY8pxP+nXwJTjeuK66DZfX18988wzeuaZZ8wOBbnUsVNnrVr7rQ7s36uGjZqoVKlSZocEII/w+bYW+tta6G9crVzPaUhPT9f48eOzPRYXF6chQ4Zcsq3T6bz2yHDdCwkJUYeOnfnCAQohPt/WQn9bC/2Nq5HrpKFIkSLasmWL/j2a6ejRo5eMg8vIyFDLli3zLkIAAADAC7gjdM48unpSQkKCOnfurO3bt0uSfvjhB3Xp0kUxMTH6448/JP3fNYC5dBcAAABQaORqTsPff/+t5cuXKzAwUPPmzVOVKlX0999/64cfftCxY8fkdDr1yiuvqHPnzurdu7d8/+fGIQAAAMD1jpu75SxXlYaYmBh98skn+vvvv5WefuGa1R9//LHuuecebd++Xffdd5/Wr1+vxo0ba+rUqUpLK7yXKwMAAACsJldJQ9WqVfXVV19p+PDheuihh/T9999rw4YNeuCBB+Tn5yfpwqSa3r17a9myZe7HAAAAABR8uZ7TEB0drdOnT2vevHl67rnn9Nhjj7mHIe3YsUO9e/dWu3btdOjQoXwLFgAAAMgvNpOWgiDXSUNaWpp2796t0NBQjR49WpMnT9a5c+eUlZWlSpUqady4cdqxY4caNLj+b04BAAAAIPdyfXO3hIQEHTlyRO3atdP69et1/Phxvfbaazp//rxCQ0NVtmxZffvtt6pevXp+xgsAAADkCx8mQucoV5WGnTt36rHHHlPFihW1efNm3XDDDRoxYoS2bt2qYcOGKS0tTV26dNHatWvlcrnyO2YAAAAAXpSrpKFJkyYaO3aszp49q48++kiSVLRoUfXp00cnT56Uw+HQwoUL9dZbb6levXrKyMjIz5gBAACAPGezmbMUBLlKGux2u3r16qUlS5Zo+fLlWrVqlSTptttu05o1a+R0Ot23Is/KylJycnL+RQwAAADAq3I9p0GSKlWqpDfeeEOVKlWSJNWuXVsjRoxQ0aJF3dvYbDbt378/b6MEAAAAYBqPkgZJatiwYbb1QYMGuf/b6XQqKCjo2qMCAAAAvIw7Qucs15dczczM1M6dO93/feutt7qfS01N1cyZM9WxY0fFxsbmfZQAAAAATONR0nCxquDj4yOn0ylJ2r17t7p27arvvvtOL7zwgsqUKZM/kQIAAAD5iInQOcv18KQiRYpkm7tw8W7QJUqUUJ8+ffTII4/IbrfnfYQAAAAATOXRnAaHw+H+b5fLpZkzZ7rX33zzTUmSn5+fevTo4Z4sDQAAAKBg8yhpyMrKcv+3zWaTv7//Jdv897//1Z49ezRnzpxrjw4AAADwEu4InTOPr550kcPh0NChQxUbG6tDhw65J0avX79ec+fOzbMAAQAAAJgr10mDy+XKdtO29PR0SdL+/fsVHh6uMmXKaODAgerSpYs6d+6c95ECAAAA+YhCQ85yffWkIkWK6P3335fT6VRkZKTKly+vrKwsBQYGat26dRo+fLg+/vhjLViwID/jBQAAACxt/fr16tSpk+rWrat7771XUVFRV9xnx44duvPOO9WiRQvNnz/f42PmOmnw8fFRq1atdPbsWS1dulSfffaZnnzySY0aNUqHDx/WHXfcoYiICA0YMMDjIAAAAABc2e+//66xY8dq1KhR2rRpk8qXL69x48YZ7hMXF6ehQ4eqa9euWrx4sVavXq1t27Z5dNxcD0+aPn26ihYtqqSkJB0+fFgfffSRjh49qp49e2r79u3avn27pAv3c0hLS9OoUaM8CgQAAAAwU0G4I3RUVJSeeeYZdenSRZLUt29fPfroo4b7rFq1SmXKlNGwYcNks9n0xBNPaNmyZWrZsmWuj+vRfRocDoccDoeio6P10Ucfyel0at26derevbv7vg3p6enKzMzMdQAAAACAlblcLrlcrmyP+fr6un9f/1uHDh2yrR8/flxVqlQxfP3Dhw+rZcuW7qSoQYMG2W6dkBu5ThqefvppSdJvv/2mxMREvfzyy/rxxx/16aefaunSpZo0aZLatWvn0cHz2ozudUw9Prxrwd7TZodgCs5za6nz7BqzQzBFl3bVzA4BXsT3Gq4XuR63n8fmzJmj2bNnZ3vsySef1PDhww33c7lcmjdvngYOHGi4ndPpVPXq1d3rQUFBio2N9ShGjy+5WqxYMd18882y2Wxq06aN2rRpo40bN2rhwoVq27atfHzMersBAACAgmfw4MEaNGhQtscuV2X4X2+88YYCAgLUp08fw+3sdnu21/Pz81NKSopHMXqcNJQqVUrdunXL9liHDh0uKZUAAAAABYlZcxpyGopkZOvWrVq0aJGWLFkih8NhuG1wcLDi4uLc68nJyVfc5395VBZYuXKlli5dqj179mR7PCUlRV27dnWv//jjj4qMjPQoEAAAAABX9scff2j06NGaMGGCatSoccXt69evn+23+cGDBxUSEuLRMT1KGt588019/vnn2rdvX7bHixQp4h4XlZmZqVdeecV9NSUAAAAAeSMlJUWDBw9W586d1alTJyUnJys5OVlZWVlyOp1KS0u7ZJ+OHTvq559/1rZt25Senq558+apbdu2Hh3X4+FJK1asuPRF/u/KSpK0fPly2e12PfLII56+NAAAAGAan+v/iqvasmWLoqKiFBUVpSVLlrgf/+677/Tggw9q7Nix6ty5c7Z9SpUqpeeee06PPvqogoKCFBAQoFdffdWj43qUNFxpnFdqaqreeustTZ8+XXa73aNAAAAAABjr3LmzDh8+fNnnNmzYkON+/fv3V9u2bRUVFaWbb75ZQUFBHh3X40qD0+nUI488oooVKyokJEShoaEKDQ2VJP3666+qXbu2WrRo4enLAgAAAKYqCJWGa1GlSpUr3tMhJ7lKGuLi4rRs2TLFxcXJ6XTq1ltvlc1mU0pKig4dOqRNmzYpPj5eH3/8sWbNmnVVgQAAAAC4PuUqaXj99de1a9cuSVK5cuX0xBNPaNWqVapYsaKaNGkiSbr55pvl7++vIUOG6OOPP2Z4EgAAAFBI5OrqSeHh4fr6669VsmRJSdKJEyc0adIkZWZmurex2+16+eWXFRAQoA8++CB/ogUAAADyic1mM2UpCHKVNBQvXjxbo8aNG6fBgwerWbNmGjlypL744gv3tqNGjdL8+fM9vsscAAAAgOuTR/dpuGjcuHEaNGiQFixYoB07dqh169bKysqSJIWFhal8+fLauHFjngYKAAAA5CcfmzlLQeBR0pCVlaUpU6bo+PHjstls+vLLLzV58mSVK1fOnTRIUqdOnbRu3bo8DxYAAACA93l0ydWuXbvK6XQqNTVVPj4+WrRokSTJ5XLJ5XK5t2vSpIkaNmyYt5ECAAAA+aiATC8whUdJw6hRoy77uMPh0NKlS3XkyBHVqlVLrVu3zpPgAAAAAJgv18OT0tPTtX79+ss+Z7PZVKVKFfXt21eS9Msvvyg1NTVvIgQAAABgqlwnDVlZWXr//fdzfN7hcKhIkQuFi5deeklffvnltUcHAAAAeImPzWbKUhDkenjSxaTghx9+0EsvvSQ/P79LtvHx8dF///tfOZ1O3XXXXXkaKAAAAABzeHzJ1dTUVHXs2FGZmZnq0qWLmjdvrqysLE2bNk1ZWVn66quv9Oijj7qrDgAAAEBB4GPSUhBc1S/7cuXKqWjRoqpSpYrsdrv8/PzcV0sKCwvTf/7znzwNEgAAAIB5cpU0ZGVlac6cOTp//rxiYmIMt+3Tp0+eBAYAAADg+pCrikhaWpoOHTqkY8eO6d13383vmAAAAACvs9nMWQqCXCUNvr6+euONN3TTTTfp5ZdfNtz2ww8/1MmTJ/MkOAAAAADm83juhc1m07lz55SWlqa//vrL/d9//PGHJCk5OVnz5s3L80ABAN5TIsChJjeWVMlAh9mhAIDXcMnVnF3VROhPPvlEDodDs2fPdj/Wo0cPFS1aVA8//LC6du2q0aNHKyAgIM8CBa4HDUKD1Kt+iEr6O/THuRR9suuUYpNcZocF5KlujUP1Su/6Ohl3XtXKBmnMokh9+ctps8PKd3y+ASBnua40ZGZmKj09Xbfffrv27dunX3755ZIlMzNTQUFBuuOOO7R27dr8jPu6cfToEfXr00ttWzXXzOlTlZWVZXZIXmHFdpcOdGhAk/Jauf+Mxn19VHHn09S/cajZYXmFFftbsma7ixUtogn31FOft39UtxlbNG7pXj3frbbZYeU7Pt/WOs8l2m21ducWcxpyluukIT09XY0bN87xeZfLpYyMDElS79691ahRo2sO7nrncrk0YtgQ1alXTwsXL9exqCitjPjC7LDynVXbXa6Yn1btP6Nd0UlKSs3QpuPxqlyiqNlh5Tur9rdV2x1UtIgmrTigIzFOSdLB6EQVDyj8Q5T4fFvrPKfd1mo38kaukwZfX1+Fh4fn+Lzdbtdbb70lSapVq5Zq1KiR6yCSk5N14sQJpaam5nqf68GWzZvkTHJq9JhwVapcWcOfGqkVy5eZHVa+s2q798U4teXEOfd6SJCvzianmReQl1i1v63a7tPnUrRy1ylJUhEfmx7rUE3f7DG+1HZhwOfbWuc57bZWu5E38uwmdHa7Xa1bt77idnfccYfOnz8vSTp9+rQee+wxNW/eXP/5z3/UpEkTvfDCC0pJScmrsPLVkcOH1KBhQ/n7+0uSaoWF6VhUlMlR5T+rtvvf7Dapc80btOlYvNmh5Dur9rdV231RnfLFtHNiZ90SVloTVxwwOxyv4vNd+M9z2m2tdnvCx2bOUhB4/c7Vv/32mzIzMyVJL7zwgux2u9avX6///ve/+uCDD/Tzzz9r1qxZ3g7rqjidTlWoUNG9brPZZLf7KDEhwcSo8p9V2/1v3euWVWp6pracKPw/Kqza31Zt90UHTyWp/3vbdSTGqdf7NjQ7HK/i8134z3PafYFV2o284fWkwfav2R47d+7UCy+8oPLlyysoKEitW7fWCy+8oHXr1nk7rKtit9vl8PXN9pivn5/+LiCVkqtl1XZfVLtMoNpWLaH5/41WpgXmj1m1v63a7n87EJ2oZxdG6rabQlTc/6outlfg8Pm2xnlOu/9hhXZ7gkuu5szrSUNWVpZ++eUX/f333woJCdG5c+eyPe9wOJScnOztsK5KcHCw4uPjsj12PjlZDkfhnjRo1XZL0g0BDg1sXl6Ld8coxiKXYrRqf1u13a1q3KDw7v9cLSn9/345W+ECK3y+L7DCeU67/2GFdiNveD1p6N+/v9555x21bdtWZ8+e1aRJk9zPffXVVxo3bpzuv/9+b4d1VerdVF97IyPd69HRJ+VyuRQcHGxiVPnPqu12+Ng0tFUlRZ5K0p7TSfKz2+RnLxh/HbgWVu1vq7Y76oxTfVtXVt9WlRRaoqie61Zbmw//qaSUdLNDy1d8vi+wynlOuy+wSruRN7yeNLzwwgtatGiRduzYoQULFqh///7u5yIjIzV06FA988wz3g7rqjRt1lxJziStXhkhSZo39321aNladrvd3MDymVXbXSckUKHF/dS2aknN7FHbvZQq5JejtGp/W7XdZxJT9eRHuzSoXVV981w7+TvsGvlZ5JV3LOD4fEdIss55TrsjJFmn3Z7gPg05s2UVort6mPGHsA3frVf4mFEKCAxUZkaGPvx4gWrUqOn9QLzsemj3qNUHvXq868WM7nW8fszrob/NcD20u86za7x6vOtFl3bVzA7BFHy+vYd2m9fuotfxFKlJ63815bgvdM79rQrMQtKQB2JjY3Vg/141bNREpUqVMicIE5jdbpIG7zK7v81idrtJGqyFz7d30W5z2n09Jw2vfmdO0jCu0/WfNFzH3VZwhISEKCQkxOwwvM6q7bYqq/a3VdsNa7HqeU67gdwjaQAAAAAk2VRAJhiYwOsToQEAAAAULCQNAAAAAAwxPAkAAACQ5MPopBxRaQAAAABgiEoDAAAAICoNRqg0AAAAADBE0gAAAADAEMOTAAAAAEk2G+OTckKlAQAAAIAhKg0AAACAmAhthEoDAAAAAENUGgAAAABJTGnIGZUGAAAAAIZIGgAAAAAYYngSAAAAIMmH8Uk5otIAAAAAwBCVBgAAAEBcctUIlQYAAAAAhkgaAAAAABhieBIAAAAg7tNghEoDAAAAAENUGgAAAABJPqLUkBMqDQAAAAAMFapKw6jVB80OwRQD6oeaHYIpZnSvY3YIpvjlxDmzQ4AXHXy9q9khmMKq57lV2934xhJmhwBIYk6DESoNAAAAAAyRNAAAAAAwVKiGJwEAAABXiztC54xKAwAAAABDVBoAAAAAST7MhM4RlQYAAAAAhkgaAAAAABhieBIAAAAg7tNghEoDAAAAAEMkDQAAAIAuTIQ2Y/FUfHy8OnbsqJMnT+Zq+yFDhigsLMy9DBw40ONjMjwJAAAAKCDi4uI0dOhQRUdH53qfffv2afXq1SpXrpwkqUgRz1MAKg0AAABAATFy5Eh16dIl19vHxMRIkmrVqqXixYurePHiCggI8Pi4JA0AAACALkyENmPxxKRJk/TQQw/levs9e/YoIyND7dq1U6NGjfTMM88oISHBw3eGpAEAAAAwlcvlktPpzLa4XK7LblupUiWPXvvEiROqV6+ePvzwQy1fvlzR0dGaOXOmxzGSNAAAAAC68MPYjGXOnDlq2rRptmXOnDl50qbHH39cc+fOVc2aNVW9enWNHj1a33zzjcevw0RoAAAAwESDBw/WoEGDsj3m6+ubL8cqXry44uPj5XK5PDoGlQYAAABAks1mM2Xx9fVVUFBQtiWvkoYRI0Zo9+7d7vW9e/eqTJkyHr8+SQMAAABQwDmdTqWlpV3yeK1atTRlyhRFRkZq48aNevPNN9W3b1+PX5+kAQAAACjgevTooR9++OGSxwcPHqzq1atr0KBBmjx5su6//34NHjzY49dnTgMAAAAgyfN7M5vn8OHD2dY3bNhw2e0cDocmT56syZMnX9PxqDQAAAAAMESlAQAAAJDk4+md1iyESgMAAAAAQyQNAAAAAAyRNFyjBqFBevn26nrrrtp69tYbFVIsf27EcT1yJiZo9MM99WfsKbND8ZqjR4+oX59eatuquWZOn6qsrCyzQ/IaK/a3ZM12c55bq78la7bbque5VdudWzaTloKApOEalA50aECT8lq5/4zGfX1UcefT1L9xqNlheUVSwjnNenmkzsaeNjsUr3G5XBoxbIjq1KunhYuX61hUlFZGfGF2WF5hxf6WrNluznNr9bdkzXZb9Ty3aruRN0garkG5Yn5atf+MdkUnKSk1Q5uOx6tyiaJmh+UV700drxbtbjM7DK/asnmTnElOjR4TrkqVK2v4UyO1Yvkys8PyCiv2t2TNdnOeW6u/JWu226rnuVXb7QmbzZylILgukgaXy6WlS5dq1qxZWrx4sU6dKhjl0X0xTm05cc69HhLkq7PJl96JrzAaODxct991v9lheNWRw4fUoGFD+fv7S5JqhYXpWFSUyVF5hxX7W7JmuznPrdXfkjXbbdXz3KrtRt4wPWlIT0/Xgw8+qPnz5+v333/XF198oTvvvDPHG1Rcr+w2qXPNG7TpWLzZoXhF2dAKZofgdU6nUxUqVHSv22w22e0+SkxIMDEq77Bif0vWbDfnufVYsd1WPc+t2m5P2Gw2U5aCwJSkYejQoTp58qQkacuWLfLz89Pq1avdlYZRo0ZpypQpZoR21brXLavU9ExtOWGNpMGK7Ha7HL7ZJ7r7+vnp75QUkyIC8h7nOazAque5VduNvGFK0lC9enX17NlTs2fPVnx8vFq2bCm73e5+/o477tDZs2fNCO2q1C4TqLZVS2j+f6OVyUUICq3g4GDFx8dle+x8crIcDodJEQF5j/McVmDV89yq7UbeMCVpGD16tD7//HNt3bpVEydO1LfffqukpCT3899//71q165tRmgeuyHAoYHNy2vx7hjFJLnMDgf5qN5N9bU3MtK9Hh19Ui6XS8HBwSZGBeQtznNYgVXPc6u22xM+Ji0FgWlx1qpVSwsXLtTzzz+v2NhYtWvXTgMHDtT999+vGTNmKDw83KzQcs3hY9PQVpUUeSpJe04nyc9uk5+9YIxLg+eaNmuuJGeSVq+MkCTNm/u+WrRsna1KBhR0nOewAque51ZtN/JGEbMDuO+++9S9e3d9++23io2NVbly5dS+ffsCkfXWCQlUaHE/hRb3U9uqJd2Pv/DNr4o7b42rKFlJkSJF9OKESQofM0ozZ0xTZkaGPvx4gdlhAXmK8xxWYNXz3Krt9kRBmZRsBltWIboV4LAVB80OwRQD6lvjhnL/q/GNJUw5bmxsrA7s36uGjZqoVKlSXj/+L/+6zC8KP85zWIFVz3OzmN3uoqb/yTpnS3abc9n/Po3Km3JcT1zH3QZcn0JCQhQSEmJ2GEC+4jyHFVj1PLdqu3FtSBoAAAAASQxOyllBmbANAAAAwCRUGgAAAAAxEdoIlQYAAAAAhqg0AAAAAOKv6UZ4bwAAAAAYImkAAAAAYIjhSQAAAICYCG2ESgMAAAAAQ1QaAAAAAHFzNyNUGgAAAAAYImkAAAAAYIjhSQAAAIAk5kHnjEoDAAAAAENUGgAAAABJPkyFzhGVBgAAAACGqDQAAAAAYk6DESoNAAAAAAyRNAAAAAAwxPAkAAAAQJKNidA5otIAAAAAwBCVBgAAAEBMhDZCpQEAAACAIZIGAAAAAIYK1fCkAfVDzQ4BXvTLiXNmhwAgn/R7Z6vZIZiiS7tqZodgisY3ljA7BEASd4Q2QqUBAAAAgKFCVWkAAAAArhYToXNGpQEAAACAISoNAAAAgKg0GKHSAAAAAMAQSQMAAAAAQwxPAgAAACTZuORqjqg0AAAAADBEpQEAAACQ5EOhIUdUGgAAAAAYImkAAAAAYIjhSQAAAICYCG2ESgMAAAAAQ1QaAAAAAHFHaCNUGgAAAAAYotIAAAAAiDkNRqg0AAAAADBE0gAAAADAEMOTAAAAAHFHaCNUGgAAAAAYotIAAAAAiInQRqg0AAAAADBE0gAAAADAEMOTAAD4PyUCHKpWNkjH/3QqPjnN7HAAeBl3hM4ZlYY84ExM0OiHe+rP2FNmh+JVtJt2W4FV221F3RqH6vtxHTSxVz1tfbGTujUONTskr2gQGqSXb6+ut+6qrWdvvVEhxXzNDgnAdYik4RolJZzTrJdH6mzsabND8SraTbutwKrtPnr0iPr16aW2rZpr5vSpysrKMjukfFesaBFNuKee+rz9o7rN2KJxS/fq+W61zQ4r35UOdGhAk/Jauf+Mxn19VHHn09TfIsmSFc9zybrtzi2bSUtBQNJwjd6bOl4t2t1mdhheR7uthXZbh8vl0ohhQ1SnXj0tXLxcx6KitDLiC7PDyndBRYto0ooDOhLjlCQdjE5U8QCHyVHlv3LF/LRq/xntik5SUmqGNh2PV+USRc0OK99Z9Ty3arsLo/j4eHXs2FEnT57M1fY7duzQnXfeqRYtWmj+/PlXdUyShms0cHi4br/rfrPD8DrabS202zq2bN4kZ5JTo8eEq1Llyhr+1EitWL7M7LDy3elzKVq568IQtCI+Nj3WoZq+2RNjclT5b1+MU1tOnHOvhwT56qwF5nJY9Ty3arsLm7i4OA0ZMkTR0dG53n7o0KHq2rWrFi9erNWrV2vbtm0eH9eUpCEiIiLXmdH1rmxoBbNDMAXtthbabR1HDh9Sg4YN5e/vL0mqFRamY1FRJkflPXXKF9POiZ11S1hpTVxxwOxwvMpukzrXvEGbjsWbHUq+s+p5btV2e8LHZjNl8cTIkSPVpUuXXG+/atUqlSlTRsOGDdONN96oJ554QsuWeZ4smpI0PP/88+rTp49eeuklnTrF5EIAuF44nU5VqFDRvW6z2WS3+ygxIcHEqLzn4Kkk9X9vu47EOPV634Zmh+NV3euWVWp6pracKPxJg1XPc6u2uyBwuVxyOp3ZFpfLddltJ02apIceeijXr3348GG1bNlStv9LTho0aKADBzz/o4hpw5NWrFih0NBQ3XvvvRo2bJg2b96sjIwMs8IBAEiy2+1y+Ga/eo6vn5/+TkkxKSLvOxCdqGcXRuq2m0JU3N8aVyavXSZQbauW0Pz/RivTAvNirXqeW7XdnjBrIvScOXPUtGnTbMucOXMuG2OlSpU8apPT6VTFiv8ki0FBQYqNjfXoNSQT79MQGBioIUOG6OGHH9bq1av11ltvafTo0erQoYNatGihRo0aqWrVqmaFBwCWFBwcrF9/PZrtsfPJyXI4Cvek4FY1blD7OmU0ZfUhSVL6//1ytsKFZW4IcGhg8/JavDtGMUmX/8tmYWPV89yq7S4IBg8erEGDBmV7zNc3by5/bLfbs72Wn5+fUq4iUTSl0mD719gtX19f9erVS0uXLtWSJUtUtWpVLV++XHfddZcZoQGApdW7qb72Rka616OjT8rlcik4ONjEqPJf1Bmn+raurL6tKim0RFE91622Nh/+U0kp6WaHlq8cPjYNbVVJkaeStOd0kvzsNvnZC8oFIK+eVc9zq7bbIyaVGnx9fRUUFJRtyaukITg4WHFxce715KtMFE1JGnK6JnCVKlU0ePBgLViwQDt37vRyVACAps2aK8mZpNUrIyRJ8+a+rxYtW8tut5sbWD47k5iqJz/apUHtquqb59rJ32HXyM8ir7xjAVcnJFChxf3UtmpJzexR272UKuSXm7XqeW7Vdltd/fr1FfmvZPHgwYMKCQnx+HVsWSbc1WPFihXq3r27ihTJ29FRP/16Lk9fDwDM1vjGEl4/5obv1it8zCgFBAYqMyNDH368QDVq1PRqDHWeXePV410vurSrZnYIppjRvY7Xj3k9nOdmuB7aXfQ6niq0LeqcKcdtWb2Ex/uEhYXpu+++c89XcDqd8vPzu6SKEBcXp/bt2+v9999Xs2bNNGzYMFWsWFEvvPCCR8czpdvuvvtuMw4LAMiFjp06a9Xab3Vg/141bNREpUqVMjskIM9Z9Ty3artzy1Zg7s98qR49emjs2LHq3LlztsdLlSql5557To8++qiCgoIUEBCgV1991ePXN6XSkF+oNAAobMyoNFwPqDRYixmVBpjneq40bI8y5/KzLarn/7yS3377TVFRUbr55psVFBTk8f7XcbcBAAAA3uPhfdYKlCpVqqhKlSpXvb9p92kAAAAAUDCQNAAAAAAwxPAkAAAAQCrA06DzH5UGAAAAAIaoNAAAAAASpQYDVBoAAAAAGKLSAAAAAKhg39wtv1FpAAAAAGCIpAEAAACAIYYnAQAAACrcd4S+VlQaAAAAABii0gAAAACIK64aodIAAAAAwBBJAwAAAABDDE8CAAAAJMYnGaDSAAAAAMAQlQYAAABA3BHaCJUGAAAAAIaoNAAAAADi5m5GqDQAAAAAMETSAAAAAMAQw5MAAAAAccVVI1QaAAAAABgqVJWGBXtPmx2CKQbUDzU7BFN0vHe82SGYIn7nbLNDAPLd58PamB2CKfq9s9XsEOBFM7rXMTsE/C9KDTmi0gAAAADAEEkDAAAAAEOFangSAAAAcLW4I3TOqDQAAAAAMESlAQAAABB3hDZCpQEAAACAISoNAAAAgLjiqhEqDQAAAAAMkTQAAAAAMMTwJAAAAEBifJIBKg0AAAAADFFpAAAAAMTN3YxQaQAAAABgiKQBAAAAgCGGJwEAAADijtBGqDQAAAAAMESlAQAAABBXXDVCpQEAAACAISoNAAAAgESpwQCVBgAAAACGSBoAAAAAGGJ4EgAAACDuCG2ESgMAAAAAQ1QaAACwuBIBDlUrG6TjfzoVn5xmdjiAabi5W86oNFyjBqFBevn26nrrrtp69tYbFVLM1+yQvMaZmKDRD/fUn7GnzA7F61bOfkIDurcwOwyvOXr0iPr16aW2rZpr5vSpysrKMjskr6Dd1mq3ZM3vtW6NQ/X9uA6a2Kuetr7YSd0ah5odkldY9d9vK3++cW1IGq5B6UCHBjQpr5X7z2jc10cVdz5N/S3yZZuUcE6zXh6ps7GnzQ7F6+6/s5lub1PX7DC8xuVyacSwIapTr54WLl6uY1FRWhnxhdlh5Tvaba12S9b8XitWtIgm3FNPfd7+Ud1mbNG4pXv1fLfaZoeV76z677eVP9+4diQN16BcMT+t2n9Gu6KTlJSaoU3H41W5RFGzw/KK96aOV4t2t5kdhteVLB6gKSPv0eHjMWaH4jVbNm+SM8mp0WPCValyZQ1/aqRWLF9mdlj5jnZbq92SNb/XgooW0aQVB3QkxilJOhidqOIBDpOjyn9W/ffbyp/v3LKZtBQE19WchkOHDikzM1NhYWGy2+1mh3NF+/7vS/aikCBfnbXIWNCBw8NVNrSCPn9/ltmheNVrI+/Rqo2R8vcr/P+oXnTk8CE1aNhQ/v7+kqRaYWE6FhVlclT5j3Zbq92SNb/XTp9L0cpdF4ZiFfGx6bEO1fTNnsL/RxGr/vtt5c83rp0plYbo6Gg98MADateuncLDwxUfH6/evXvroYce0v3336/bbrtNBw8eNCO0q2a3SZ1r3qBNx+LNDsUryoZWMDsEr2vXrKY63FxL499caXYoXuV0OlWhQkX3us1mk93uo8SEBBOjyn+0+wKrtFuy5vfaRXXKF9POiZ11S1hpTVxxwOxwvMpK/35b+fOda5QacmRK0vDCCy+ocuXKmjVrllwul/r06aM2bdpo+/bt+vHHH1WxYkWNHz/ejNCuWve6ZZWanqktJwr/l44V+fkW0ezxfTVi8mIlJaeYHY5X2e12OXyzTxD09fPT3ymF+32g3f+wQrut7uCpJPV/b7uOxDj1et+GZofjVVb695vPN66FKUnDL7/8ouHDh6tp06aaMGGCTp48qSFDhkiSgoKC1KdPHx05csSM0K5K7TKBalu1hOb/N1qZXISgUAp/7E79vP83fb1lv9mheF1wcLDi4+OyPXY+OVkOR+EeokW7/2GFdkM6EJ2oZxdG6rabQlTc/7oavZxvrPbvN5/vK7OZ9L+CwJSkoUSJEjp37pwk6eTJk8rKylLUv8bUnT17VmXKlDEjNI/dEODQwObltXh3jGKSXGaHg3xy351N1a19A53eNE2nN03TfXc205vh9+mN8D5mh5bv6t1UX3sjI93r0dEn5XK5FBwcbGJU+Y92X2CVdltVqxo3KLz7P1dLSv+/X85WuAqnFf/95vONa2HKnxKeeuopDR06VI0aNdK2bdt03333adiwYerSpYv++usvrVu3To8++qgZoXnE4WPT0FaVFHkqSXtOJ8nPfiFTTM2wwLetxXR++A3Z7f/k2K+NvFs79pzQp6u3mRiVdzRt1lxJziStXhmh7nf11Ly576tFy9YF4mIF14J2W6vdVhV1xqk5jzTVibPJ+v7gnxrVJUybD/+ppJR0s0PLV1b995vPN66FKUlDz5491bRpUx04cEBPPvmkqlevrt27d+urr75S8eLF9eqrr6pLly5mhOaROiGBCi3up9DifmpbtaT78Re++VVx5wv/VRisJPrMuWzrzvOpOnvOqb/OJZsTkBcVKVJEL06YpPAxozRzxjRlZmTow48XmB1WvqPd1mq3VZ1JTNWTH+3S+J51Fd6jjjYfOquRn0VeeccCzqr/fvP5vjLuCJ0zW1YhuhXgsBUF64pLeWVA/cJ/Q5rL6XhvwZosn1fid8425bixsbE6sH+vGjZqolKlSpkSgxlotznt/uXEOa8f83rQ752tZodgii7tqpkdgilmdK9jynHN/nwXvY6nzPx65m9TjlujrL8px/XEddxtAK4nISEhCgkJMTsMr6PdAAobPt85o9CQM+4IDQAAAMAQSQMAAAAAQwxPAgAAACTGJxmg0gAAAADAEJUGAAAAQCowd2c2A5UGAAAAoAA5cuSIevXqpebNm2vq1KnKzR0UhgwZorCwMPcycOBAj45J0gAAAAAUEC6XS0OGDFG9evW0fPlyRUVF6Ysvvrjifvv27dPq1au1c+dO7dy5U++++65HxyVpAAAAAHThjtBmLJ7YtGmTnE6nwsPDVblyZY0cOVLLli0z3CcmJkaSVKtWLRUvXlzFixdXQECAR8claQAAAABM5HK55HQ6sy0ul+uy2x46dEgNGzaUv/+Fu0iHhYUpKirK8PX37NmjjIwMtWvXTo0aNdIzzzyjhIQEj2IkaQAAAAB04YqrZixz5sxR06ZNsy1z5sy5bIxOp1MVK1b8J2abTT4+PoZJwIkTJ1SvXj19+OGHWr58uaKjozVz5kyP3huungQAAACYaPDgwRo0aFC2x3x9fS+7rd1uv+Q5Pz8/paSkKDg4+LL7PP7443r88cfd66NHj9aIESP08ssv5zpGkgYAAABAMu3mbr6+vjkmCf8rODhYR48ezfZYcnKyHA5Hro9XvHhxxcfHy+Vy5fq4DE8CAAAACoj69esrMjLSvX7y5Em5XK4cqwySNGLECO3evdu9vnfvXpUpUybXCYNE0gAAAAAUGM2bN1dSUpIiIiIkSe+//75at24tu90up9OptLS0S/apVauWpkyZosjISG3cuFFvvvmm+vbt69FxGZ4EAAAAqGDcEbpIkSKaNGmSRo0apWnTpikjI0MLFiyQJPXo0UNjx45V586ds+0zePBgnTp1SoMGDdINN9yg+++/X4MHD/bsuHnWAgAAAAD5rnPnzvr222+1d+9eNWnSRKVKlZIkbdiw4bLbOxwOTZ48WZMnT77qY5I0AAAAAPL8RmtmCgkJUUhIiNeOx5wGAAAAAIZIGgAAAAAYYngSAAAAINNu01AgUGkAAAAAYIhKAwAAAKCCNRHa26g0AAAAADBEpQEAAACQxKyGnNmysrKyzA4ir/z06zmzQwCQTxrfWMLsEOBFv5w4Z3YIpliw97TZIZhiRvc6ZodgijrPrjE7BFMcn9XV7BBydDLeZcpxK5b0NeW4nmB4EgAAAABDDE8CAAAAxERoI1QaAAAAABii0gAAAACIadBGqDQAAAAAMETSAAAAAMAQw5MAAAAAMRHaCJUGAAAAAIaoNAAAAACSbEyFzhGVBgAAAACGqDQAAAAAEtdcNUClAQAAAIAhkgYAAAAAhhieBAAAAIjRSUaoNAAAAAAwRKUBAAAAEDd3M0KlAQAAAIAhkgYAAAAAhhieBAAAAIg7Qhuh0gAAAADAEJUGAAAAQOKaqwaoNAAAAAAwRKUBAAAAEIUGI1QaAAAALKREgENNbiypkoEOs0NBAULSkAeciQka/XBP/Rl7yuxQvIp2026gsLHied4gNEgv315db91VW8/eeqNCivmaHRLyUbfGofp+XAdN7FVPW1/spG6NQ80OCQUEScM1Sko4p1kvj9TZ2NNmh+JVtJt2W8HRo0fUr08vtW3VXDOnT1VWVpbZIXmFVdttxfO8dKBDA5qU18r9ZzTu66OKO5+m/hb5EWnF87xY0SKacE899Xn7R3WbsUXjlu7V891qmx3WdcVmM2cpCEgartF7U8erRbvbzA7D62i3tVix3S6XSyOGDVGdevW0cPFyHYuK0sqIL8wOK99Ztd2SNc/zcsX8tGr/Ge2KTlJSaoY2HY9X5RJFzQ4r31n1PA8qWkSTVhzQkRinJOlgdKKKBzBECblD0nCNBg4P1+133W92GF5Hu63Fiu3esnmTnElOjR4TrkqVK2v4UyO1Yvkys8PKd1Ztt2TN83xfjFNbTpxzr4cE+epscpp5AXmJVc/z0+dStHLXhaF3RXxseqxDNX2zJ8bkqK4vNpP+VxCYdvWkhIQE2e12BQUFmRVCnigbWsHsEExBu63Fiu0+cviQGjRsKH9/f0lSrbAwHYuKMjmq/GfVdkvWPM//zW6TOte8QRt+jTM7lHxn5fNckuqUL6bPn2iptIxMdZryg9nhoIDweqUhPj5e/fv3V8uWLdW8eXPdd999Wr16tSXGEgIoOJxOpypUqOhet9lsstt9lJiQYGJU+c+q7YbUvW5ZpaZnasuJeLNDyXdWP88PnkpS//e260iMU6/3bWh2OCggvJ40vPLKKwoKCtKXX36pNWvWqHnz5nr22WfVrVs3bdiwwdvhAMBl2e12OXyzX0XG189Pf6ekmBSRd1i13VZXu0yg2lYtofn/jVamBf6Gx3kuHYhO1LMLI3XbTSEq7s9tuy5iInTOvJ40bN68WRMmTFD16tVVrVo1jR49Wo0aNVKzZs00fvx4PfDAA9q/f7+3wwKAbIKDgxUfn32YxvnkZDkchXvSoFXbbWU3BDg0sHl5Ld4do5gkl9nheIVVz/NWNW5QePd/rpaU/n8ZIoM9kBteTxpCQkJ04sSJbI+5XC49+uij2rBhg2655RY98sgj3g4LALKpd1N97Y2MdK9HR5+Uy+VScHCwiVHlP6u226ocPjYNbVVJkaeStOd0kvzsNvnZC8ifPa+BVc/zqDNO9W1dWX1bVVJoiaJ6rlttbT78p5JS0s0ODQWA15OGgQMHatiwYXrttdf08ccf66GHHlLRokVVqVIlFS1aVI8//rjWrVvn7bAAIJumzZoryZmk1SsjJEnz5r6vFi1by263mxtYPrNqu62qTkigQov7qW3VkprZo7Z7KVXIL8Np1fP8TGKqnvxolwa1q6pvnmsnf4ddIz+LvPKOgCRblgkzkH/88UetXLlS8fHxqlOnjh5++OE8ye5/+vXctQcH4LrU+MYSXj/mhu/WK3zMKAUEBiozI0MffrxANWrU9Hoc3nY9tPuXf10G1EoW7LXOjeX+bUb3Ol4/5vVwntd5do1Xj3e9OD6rq9kh5Ojc3xmmHLeE//WfsJqSNOQXkgag8DIjaZCk2NhYHdi/Vw0bNVGpUqVMicEMZrebpMFazEgaJPPPc5KG6w9JQ86YLg8ABkJCQhQSEmJ2GF5n1XbDWjjPgdwjaQAAAACkAnN3ZjN4fSI0AAAAgIKFSgMAAACggnOjNTNQaQAAAABgiKQBAAAAgCGGJwEAAAAS06ANUGkAAAAAYIhKAwAAACBRajBApQEAAACAISoNAAAAgLi5mxEqDQAAAAAMkTQAAAAAMMTwJAAAAEDcEdoIlQYAAAAAhqg0AAAAAOKKq0aoNAAAAAAwRNIAAAAAwBDDkwAAAACJ8UkGqDQAAAAAMESlAQAAABB3hDZCpQEAAAAoQI4cOaJevXqpefPmmjp1qrKysq64z44dO3TnnXeqRYsWmj9/vsfHJGkAAAAACgiXy6UhQ4aoXr16Wr58uaKiovTFF18Y7hMXF6ehQ4eqa9euWrx4sVavXq1t27Z5dFySBgAAAEAX7ghtxuKJTZs2yel0Kjw8XJUrV9bIkSO1bNkyw31WrVqlMmXKaNiwYbrxxhv1xBNPXHGf/0XSAAAAAJjI5XLJ6XRmW1wu12W3PXTokBo2bCh/f39JUlhYmKKiogxf//Dhw2rZsqVs/5ehNGjQQAcOHPAoxkI1EbpVjRJmhwAAyANW/T63arut6visrmaHgP9R1KRfxm+/PUezZ8/O9tiTTz6p4cOHX7Kt0+lUxYoV3es2m00+Pj5KSEhQcHDwZV/f6XSqevXq7vWgoCDFxsZ6FGOhShoAAACAgmbw4MEaNGhQtsd8fX0vu63dbr/kOT8/P6WkpOSYNPzvPhe39wRJAwAAAGAiX1/fHJOE/xUcHKyjR49meyw5OVkOh8Nwn7i4uFxvfznMaQAAAAAKiPr16ysyMtK9fvLkSblcrhyrDJfb5+DBgwoJCfHouCQNAAAAQAHRvHlzJSUlKSIiQpL0/vvvq3Xr1rLb7XI6nUpLS7tkn44dO+rnn3/Wtm3blJ6ernnz5qlt27YeHdeWlZu7QQAAAAC4Lqxfv16jRo1SYGCgMjIytGDBAtWsWVMdO3bU2LFj1blz50v2+eyzzzRlyhQFBQUpICBAS5YsUenSpXN9TJIGAAAAoICJjY3V3r171aRJE5UqVSpX+/z222+KiorSzTffrKCgII+OR9IAAAAAwBBzGgAAAAAYImkAkCvx8fHatWtXtku2AQAAayBpuEZHjhxRr1691Lx5c02dOlVWGu0VHx+vjh076uTJk2aH4jXr169Xp06dVLduXd17771XvG17YbFmzRrdfvvtmjhxojp06KA1a9aYHZJXPfLII/riiy/MDsNrJk2apLCwMPdy2223mR2SV02fPl1DhgwxOwyv+OKLL7L19cXFCud7RESE2rdvr8aNG2vgwIGW+bds+fLl6tatm5o1a6aRI0fyhyDkGknDNXC5XBoyZIjq1aun5cuXKyoqyhJftJIUFxenIUOGKDo62uxQvOb333/X2LFjNWrUKG3atEnly5fXuHHjzA4r3yUmJmrSpEn67LPPFBERoZdfflnTp083OyyvWbVqlbZs2WJ2GF61f/9+vf/++9q5c6d27typFStWmB2S1xw5ckSff/65xo4da3YoXtGtWzd3P+/cuVM//PCDSpYsqebNm5sdWr76/fff9cYbb+idd97RmjVrVL58eYWHh5sdVr778ccf9corryg8PFwrV66U0+nUk08+aXZYKCBIGq7Bpk2b5HQ6FR4ersqVK2vkyJFatmyZ2WF5xciRI9WlSxezw/CqqKgoPfPMM+rSpYtKly6tvn37at++fWaHle+Sk5M1duxY1apVS5JUu3ZtJSQkmByVd5w7d05Tp05V1apVzQ7Fa9LT03XkyBE1a9ZMxYsXV/HixT2+wkZBlZWVpRdffFEPPfSQKleubHY4XuHr6+vu5+LFiysiIkK33367KlWqZHZo+erAgQNq2LCh6tWrp/Lly+uee+7R8ePHzQ4r30VEROjee+9VmzZtVKFCBY0ZM0Y///yz4uPjzQ4NBQBJwzU4dOiQGjZsKH9/f0lSWFiYZYarTJo0SQ899JDZYXhVhw4d1LdvX/f68ePHVaVKFRMj8o7Q0FD16NFDkpSWlqZ58+bp9ttvNzkq75g6dao6d+6sRo0amR2K1xw+fFhZWVnq2bOnGjRooEceeUSnTp0yOyyvWLJkiQ4dOqSKFStq48aNl71BUmGWmpqqTz75RI8//rjZoeS7GjVqaNu2bTpw4ICSkpL0+eefq02bNmaHle/i4+NVvnx597qPz4WfgUWKFDErJBQgJA3XwOl0qmLFiu51m80mHx8fS/wVtrD/FepKXC6X5s2bp379+pkditccOnRIbdq00datWy0xdGPbtm366aef9Oyzz5odildFRUWpZs2amjFjhtasWSOHw6EXX3zR7LDyXXJyst544w1VqVJFMTExmj9/vgYMGKDU1FSzQ/Oa1atXq2HDhtn+XSusatSooTvuuEN33323mjVrpsjISD333HNmh5Xv6tSpo++++849//KLL75QgwYNVKxYMZMjQ0FA0nAN7Ha7fH19sz3m5+enlJQUkyKCt7zxxhsKCAhQnz59zA7Fa8LCwvTRRx+pRo0ahX7sb2pqql566SVNmDDBMkNzLurRo4eWLFmiBg0aqFKlSnrhhRe0detWOZ1Os0PLV+vWrdPff/+tjz/+WMOGDdO8efOUmJioiIgIs0PzmkWLFun+++83Owyv2L17tzZu3KilS5dq165d6tatmx577LFCfzGThx9+WGlpabrnnnt0//3364MPPlD//v3NDgsFBEnDNQgODr7kqgPJyclyOBwmRQRv2Lp1qxYtWqQZM2ZYqq9tNpvq1q2r1157Td99912hrqi9++67uummm9S+fXuzQzFd8eLFlZmZqTNnzpgdSr6KiYlRgwYNVKJECUkXhmuEhYVZ5oo6v/32m37//Xe1bt3a7FC8Yu3ateratasaNGigwMBAPf300zp58qQOHTpkdmj5qkSJElq0aJHeeOMN1apVS9WqVVP37t3NDgsFBEnDNahfv74iIyPd6ydPnpTL5VJwcLCJUSE//fHHHxo9erQmTJigGjVqmB2OV/z000+aOnWqe91ut0v6ZyxsYbR69Wpt2LBBzZo1U7NmzfTll1/q5Zdf1oQJE8wOLd9NmTJFa9euda/v3btXPj4+Cg0NNTGq/FeuXLlLhiKdOnUq2/jvwuyrr75S+/btLfOHkIyMDJ09e9a9npycrPPnzysjI8PEqLynbNmyWrdunUaNGuX+TgeuhJkv16B58+ZKSkpSRESEevbsqffff1+tW7fmA1hIpaSkaPDgwercubM6deqk5ORkSVJAQIBsNpvJ0eWfatWqadiwYbrxxhvVrl07vfHGG2rTpk2hHgP7+eefKz093b0+bdo0NWzYUHfffbeJUXlHnTp19MYbb6hMmTJKT0/XpEmTdPfdd7sv+FBYtW/fXq+88ooWLlyoDh066Ntvv9XBgwc1c+ZMs0Pzis2bN+uee+4xOwyvadKkicaOHauPPvpIN9xwg5YuXarSpUsrLCzM7NC84tNPP1W1atXUuXNns0NBAWLLKuwD+PLZ+vXrNWrUKAUGBiojI0MLFixQzZo1zQ7La8LCwvTdd99ZYuLc+vXrNWzYsEset0L7N2/erClTpigmJkZt27bVhAkTVKpUKbPD8prnn39eN998s2V+VM2YMUOLFi1SYGCgOnfurJEjRyogIMDssPLd7t279dprr+ngwYMqXbq0wsPDLfGjKiUlRc2aNdPKlStVvXp1s8PxiqysLL3zzjtavny5/vzzT9WsWVOTJk3STTfdZHZo+S4xMVG33XabPvjgAzVo0MDscFCAkDTkgdjYWO3du1dNmjSx1A8pAAAAWANJAwAAAABDhXcmIwAAAIA8QdIAAAAAwBBJAwAAAABDJA0AAAAADJE0AAAAADBE0gAAecTlcl1yR9msrCy5XC6PXicrK0uZmZl5ElN6erp27NhxyesdO3ZM58+fz5NjAAAKPy65CgBX4ZZbblFgYKD8/PyUlJSk//znP4qNjdXBgweVlpam2NhYVa1aVZmZmUpLS9PatWv1xBNPqE+fPurYsaN27Nihf3/9li5d2n1jrY8++kh79uzJdjfiWbNmKS0tTWPGjFFGRobS09Pl5+eXLaa0tDT5+Phkuyv98uXL9eqrr+qDDz5QcHCwHA6HKlSooDvvvFMPPvigHnjgAWVlZSk9PV0OhyOf3zUAQEFVxOwAAKAg2rx5syTp5MmTuvfee3X33Xe77wb/ww8/aO7cufr000+z7dOrVy8999xzevvttzVkyBB17dpVkvTbb7+pUqVKGjFihBISEuTr6ytfX99s+/r7+7uTge3bt+vxxx9XYGCg+/n09HT9/fffmj9/vlq0aCFJ+uOPP/T666+rfPnyevTRR1WjRg01atRIFSpU0NmzZ/Xee+9p5syZKlasmNq2bavJkyfnz5sFACjwSBoA4Cqkpqbq3Xff1e+//67hw4e7EwZJOnPmjKpUqXLJPrfddpsaNGigsmXLys/PT+PHj9epU6cUGRmpn3/+WVu2bNGuXbvUoEED9z5//fWXkpKSlJSUJJfLpePHj6t+/frat2+fYXy7d+/W008/rX79+mnQoEG65ZZb9MEHH+jMmTMaNGiQFi9eLF9fXz388MP69ttvVaQI/xwAAHLGvxIAcJUOHz6sX375RVOnTpV04Yf6+PHjFR8fr4yMDO3evVuS9MADD+iOO+7Q999/r549e7r3j42NVe/evfXCCy9Ikux2u3x8sk81+/HHH7Vjxw6tX79e5cuXV3Jysvr06aNGjRpdNqaMjAzZ7XZVrlxZTz/9tPt469at07p16/T666/rtddeU61atSRJr7766iXHBADgf5E0AICHMjIyZLPZ9Pbbb7vnGvj6+io9PV0lS5bUl19+qcOHD6tSpUp69913lZycrLi4OL399tvavXu3O0nw8/NTsWLFDI/VvXt3de3aVWvXrlX79u01cOBA9e/fX9HR0SpSpIjS09OVlZUlh8OhtLQ0BQYGavPmzSpVqpQ7Yfj77781b948rV27VpMmTdK7776rQ4cOaeDAgWrVqlV+v10AgEKApAEAPHTgwAGNGjVKknT69Glt2rRJR48e1axZs9zbPPLII5o7d64kycfHR9WqVdPChQs1f/5891/2s7KyVLRo0Sse76effpLT6dTKlSt18OBBrVixwj2/4d1339X58+c1evTobPtkZWXp8OHD+vbbb7VkyRK1bt1aK1asUKlSpdShQwd98skn6t27t2699VbdeuutqlWrlkqVKpUn7w8AoPAhaQAAD9WvX1/ffvutoqKi9NRTT+ntt9/Wo48+qvLly7u3SU1NVeXKlbPtV7ZsWQ0cOFA2m01ZWVmKj49XiRIlJMnwEquffvqpKleurNtuu00RERHasWOHzp8/r5tuusm9zfHjx3Xu3Dk1btxYkjR//ny9+eab6tq1q/r166d3331XGzdudCcsqampuvnmm1W3bl198sknSk9P15w5c2Sz2fLqbQIAFCIkDQBwjcqUKaP33ntPycnJkqSkpCT5+voqICBAkrJdWvXpp5/Wfffdp5SUFP32228qW7aspAuXS73c/Rk2btyo/fv3q0+fPpKkmTNnqk6dOrrtttvccymkC3MWFi9erC+//FL+/v4aMGCA7r33XhUrVkwul0tPPPGEoqOj9ccff6hly5aSpEWLFqlp06bq3r17/r05AIBCgdlvAHCNgoKCVKtWLfdN3Pbt26eKFSu6n09NTZUkHTp0SL/++qs6deqkr776Sps3b1a9evXUpk0bTZo0SRkZGUpLS8v22osXL9Yrr7zividDy5YttW3bNpUuXVrt27d3bzdo0CD5+fnp3XfflST5+vqqWLFi+uuvv9S9e3f3vSNef/11SRcmYb/yyitKT0/Pt/cFAFB4kDQAQB4JCwvTc889p8WLF+uWW27Rt99+qwcffFD33XefJOn//b//pwceeEDFihWTn5+fvv76awUGBqpv3746f/68/vOf/+jZZ5/N9pqzZ8/Wrbfe6l5PT0/X22+/7R7mdJHD4dCQIUO0cOFCJSUluR+/4YYbVLNmTa1bt04NGzbU8ePH5XQ69fXXX6tTp06XDKECAOBySBoA4CokJSXp9OnT2e6+XKxYMa1bt04HDx7UAw88oPXr1+uZZ55RWlqaNm3apI0bN+qBBx6QJE2dOlUdOnTQgw8+qObNmys8PFzFixdXSEiIuzIhyX3/hLS0NGVkZCg1NVXdu3dXly5dtH79ev3888/uydRdunTRl19+6b4i08WhTkOGDFGNGjVkt9v10UcfydfXV8uWLVPv3r0liWoDAOCKmNMAAFfhww8/VEREhB599FFJF4YkjRs3TqVLl9bChQsVHBysadOmaeHChbr//vv12muvafDgwSpZsqQiIiL0008/afny5ZKksWPH6rnnnlNSUpLmz5+v999//5KrIblcLqWnpyswMFCDBw+WJM2dO1fFihVzz0koUqSIypUr597npptuUkBAQLbE5t+vN2rUKLlcLrlcLm3btk3FixfPl/cKAFDw2bL+PUMPAHBVMjMztXv3bjVp0uSS506fPq3Q0FD3elZWls6cOaOQkJBLtv3zzz/lcrlUoUKFfI0XAABPkDQAAAAAMMScBgAAAACGSBoAAAAAGCJpAAAAAGCIpAEAAACAIZIGAAAAAIZIGgAAAAAYImkAAAAAYIikAQAAAICh/w8JpDTD1HhoWwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 Random Forest...\n",
      "最佳参数: {'max_depth': None, 'n_estimators': 100}\n",
      "训练集准确率: 1.0000\n",
      "测试集准确率: 0.2700\n",
      "交叉验证准确率: 0.2925 ± 0.0384\n",
      "训练时间: 2.26 秒\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 KNN...\n",
      "最佳参数: {'n_neighbors': 3, 'weights': 'distance'}\n",
      "训练集准确率: 1.0000\n",
      "测试集准确率: 0.2800\n",
      "交叉验证准确率: 0.2300 ± 0.0350\n",
      "训练时间: 0.42 秒\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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H/AIAAADniPbt2ysxMVFz587VwYMHNWfOHG3fvl1XXHFFiZ5P8w8AAACcI8LDw/Xmm29q6dKl6tKli2bPnq0pU6YoOjq6RM9n2Q8AAAAgSS6X6QQl0rx5c82bN++0nsvMPwAAAGAJZv4BAAAAydgBv07y/woBAAAASKL5BwAAAKzBsh8AAABAOmcO+D0TzPwDAAAAlmDmHwAAAJA44BcAAACA/2DmHwAAAJBY8w8AAADAf9D8AwAAAJZg2Q8AAAAgccAvAAAAAP/BzD8AAAAgccAvAAAAAP9B8w8AAABYgmU/AAAAgMQBvwAAAAD8BzP/AAAAgGTFAb80/2fJuAd7qNGF1dX74emmo8CHtm1cr5WzX1XabymqcWGMbrr/CUVG1zEdCz7CeAP+a+IdrTSoc0zR/T0pGWo54gODiZyTnnZUIwffpnFTZiiqeg3TceAwlv2cBRfXq65BN1+hRycvMh3FMbt371L/Pjep3eVxenHyRBUWFpqO5HO/H0zW4mkT1aX/ID3++kKFV4nSkumTTMdyBOPNeNuAuu2qu/kFEeoz6VPVGbRAdQYt0FVPrjQdyRHpR1P1bPxDOnRwv+koZZMrwMzNQTT/Z8HUp/pq6nufaW/Sb6ajOMLr9erBB4aoUePGmjt/kfYkJmppwmLTsXzucPI+XdNvoJq26aCK4RFq3bmnkhN3mo7lc4w3423DeFO3XXW7A1y6qFa4vtxxSOlZuUrPypUnO890LEdMHhuvdh27mI4Bg8pE85+RkaFDhw4pNTX1nJtxuPvGtmoWE62fk3/XdVc0UblyZeJX6lPr162VJ8OjkY/Fq1bt2hr20HAtWbTQdCyfu6hlG7Xu3LPo/uH9v+j8ajUNJnIG430c4+3fqNuuuhvXDpdL0tp/dtX+Wbfo/cc6KPr8CqZjOeL+kU+pe+/+pmPAIGNr/pcsWaL3339fiYmJcrvdCgoKUmZmpnJzc3X55Zdr5MiRqlevnql4JRIaEqRnHuimn345rJpR4ep/fZweH9hFXe79l3K8/juDsGvnDjWLjVVISIgkqWFMjPYkJhpO5ay8vFytXzZfba+/2XQUn2O8GW8bxpu67ao7pkaYdiSn6fE5G/V7Ro4m3t5KU+5urZsnfWo6ms9VqxFtOkLZxgG/vjFp0iQlJibq6aef1kUXXVTsa0lJSZoxY4Zuu+02rVy5Uuedd56JiCXS8+rmCg0J0nWDXlZqepYmzfpYmxaM0q3dWmvW4i9Mx/MZj8ejmjX/ePNwuVxyuwOUnpamymFhBpM5Z/W8mQoqH6K4Tt1NR/E5xpvxtmG8qfs4W+p+/8uf9f6XPxfdf2z2Jn03pYcqhZRTxjH/nbwDJEPLfhYuXKgnn3zypMZfkqKjozV27FgFBATou+++M5Cu5GpGhmvjlp+Vmp4lScrPL9CPu5N1Qc3zDSfzLbfbrcCgoGKPBQUH61h2tqFEzvrph03asOoD3fLQaLnL+f8JsxhvxtuG8abuP9hQ939Ly/LKHRCgqPAQ01FgGgf8+katWrU0a9Ys5eTknPLrS5cuVWZmppo0aeJwstJJSklVSPnib5q1q0folwNHDCVyRlhYmFJTi9eYlZmpwMBAQ4mccyRlvxa8PF49Bj6iyOgLTMdxBOPNeNsw3tT9BxvqHn9rC/VqXbvo/iUXnq/8ggIl/55lMBXgDCPTWP/85z913333aeXKlWrevLmio6MVFBSkI0eOaPPmzfJ4PJo8ebIiIyNNxCuxj9Zt1YuP36yBvdvpw7U/qufVsWoWE62Pn3jLdDSfatykabEDwpKTk+T1ehXmx38ilqRcb47mTIhXo7h2ahTXVjnZxz8kgoJD5PLjNYKMN+Ntw3hT93G21L1lX6qeujlWh45my+12aeIdrfTe2j065s03HQ3wOSPNf8OGDfXhhx9q7dq12rFjhzIzM+V2u1W/fn3dcMMNiouLk9vtNhGtVFLTs9TzgWmaMPwGTRx+o1J+T9cdT7zl9zP/LVvFKcOToWVLE9S9Zy/NenOGWl/W5pwYszOxe/MGHU7ep8PJ+7RpzfKix0dOnavzIqsbTOZbjDfjbcN4U7dddc9fv1cxNcL07vAr5TmWp+X/+VXjFmw2HQtlgcNLcExwFZ5r59b8H0IuGWo6ghGpG6c6vs1P1qxW/GMjVCE0VAX5+Zo5+x3Vr9/A0QzLtx5wdHtlRbfGzjeejLc5to63CdRtru7qd73r6PbKii8n9TIdwYhG1UNNR/hLIVeNNbLdY58/7di2/P/oNfhEx6s76YOVH2vb1i2Kbd5CERERpiPBhxhvu9g63tRtV93AKQX477LOE2j+cdqioqIUFRVlOgYcwnjbxdbxpm4A/o7mHwAAAJCsWPPv/xUCAAAAkETzDwAAAFiDZT8AAACAJPnxdVxOYOYfAAAAsAQz/wAAAIDEAb8AAAAA/AfNPwAAAGAJlv0AAAAAEgf8AgAAAPAfzPwDAAAAEgf8AgAAAPAfzPwDAAAAEmv+AQAAAPgPmn8AAADAEiz7AQAAACQO+AUAAADgP5j5BwAAACQO+AUAAADgP2j+AQAAAEuw7AcAAACQOOAXAAAAgP9g5h8AAACQOOAXAAAAgP/wq5n/1I1TTUcwYu/hTNMRjGgcWdl0BCNsHe9P9xw1HcGIbo2rm45gxPKtB0xHMMLW97UDb99qOgJwHGv+AQAAAPgLmn8AAADAEn617AcAAAA4bSz7AQAAAOAvmPkHAAAAJE71CQAAAMB/0PwDAAAAlmDZDwAAACBxwC8AAAAA/8HMPwAAACBxwC8AAAAA/0HzDwAAAFiCZT8AAACAxAG/AAAAAPwHM/8AAACAxAG/AAAAAPwHM/8AAACAJBcz/wAAAAD8Bc0/AAAAYAmW/QAAAABi2Q8AAAAAP8LMPwAAACBJ/j/xz8w/AAAAYAuafwD4H0KD3KobEaLQILfpKAAAnDGa/zO0e/cu9e9zk9pdHqcXJ09UYWGh6UiOSU87qkF9uynlwH7TURxF3fbU3bJmZY25pp5uia2m8V3qq2XNyqYjOcLW97VtG9dr8tB+Gt23o14bdZ8OJe0zHckxNu7ftr7Oba27pFwul5Gbk2j+z4DX69WDDwxRo8aNNXf+Iu1JTNTShMWmYzki/Wiqno1/SIcO2vNBIVG3TXWHBAbo5tgovbh2nyZ8uldzNx9QryaRpmP5nK3va78fTNbiaRPVpf8gPf76QoVXidKS6ZNMx3KEjfu3ra9zW+tGcTT/Z2D9urXyZHg08rF41apdW8MeGq4lixaajuWIyWPj1a5jF9MxHEfd9gguF6BFP6ToQEaOJCkpLUchgf7/lmnr+9rh5H26pt9ANW3TQRXDI9S6c08lJ+40HcsRNu7ftr7Oba27NJj5x/+0a+cONYuNVUhIiCSpYUyM9iQmGk7ljPtHPqXuvfubjuE46rbH0WN52piULkkKcEmdGkTo+/0ZhlP5nq3vaxe1bKPWnXsW3T+8/xedX62mwUTOsXH/tvV1bmvdKI7m/wx4PB7VrBlddN/lcsntDlB6WprBVM6oViP677/JD1G3fWpWDtaErg3VKLKiFm5JMR3H52x+XzshLy9X65fNL/aPAX9m4/5t6+vc1rpLg5l//E9ut1uBQUHFHgsKDtax7GxDiQCcbcnpOXp5/T4dSM/RbS1qmI7jc7yvSavnzVRQ+RDFdepuOgp8xNbXua11ozgjF/nav79kBxXVqFG2P2jDwsL000+7iz2WlZmpwMBAQ4kA+EJSWo7mfLtf47rUV0hggI7lFpiO5DO2v6/99MMmbVj1gYY8O03uclwH01/Z+jq3tW4UZ+Sd7fbbby/6B8BfnWLK5XJp+/btTsYqtcZNmhY7UCY5OUler1dhYWEGUwE4GxpWqaDG1SpqyY+HJEkF/9fv+/tZ8Wx+XzuSsl8LXh6vHgMfUWT0BabjwIdsfZ3bWndpOL0ExwQjy34WLlyo2NhYjRo1Sjt27Djlraw3/pLUslWcMjwZWrY0QZI0680Zan1ZG7ndXAwIONeleLxqe0G42l4QrvCQcurZpKp2HMpUdp7/zvpL9r6v5XpzNGdCvBrFtVOjuLbKyc5STnYW50D3U7a+zm2tG8UZmfk/77zz9Nprr2n48OHq0KGDoqPPzYONypUrp6fHjFP8YyP04gvPqyA/XzNnv2M6FoCzIC07TzM3JOumplG6oUmkth/K1OxN/n8edFvf13Zv3qDDyft0OHmfNq1ZXvT4yKlzdV5kdYPJ4Au2vs5trbtU/H/iX65CP5rWyM4zs92UlBRt27pFsc1bKCIiwvHt7z2c6fg2AadN/foX0xGMeKF7IyPbNf2+tnzrAce3WRY0jrTjKtL/rW7VUCPbNf06N8V03eXL8OE0Yf3/bWS7ae/d7ti2yvCv/9wRFRWlqKgo0zEA4KzhfQ02sPV1bmvd/mLcuHF6550//mJTu3ZtrVq1qsTPp/kHAAAAdG4c8Lt161bNmDFDl1xyiSQpIKB0h/DS/AMAAADngLy8PO3atUutWrVSaOjpLZfjIl8AAACAyv4Vfnfu3KnCwkL16tVLzZo10z333FPi62edQPMPAAAAGOT1euXxeIrdvF7vSd+XmJioBg0a6IUXXtCKFSsUGBiop59+ulTbYtkPAAAAIHNr/qdPn66pU6cWe2zo0KEaNmxYscd69OihHj16FN0fPXq0OnXqJI/Ho4oVK5ZoWzT/AAAAgEGDBw/WgAEDij0WFBT0t8+rXLmyCgoKdOjQIZp/AAAA4FwQFBRUomb/ueeeU2xsrLp27SpJ2rJliwICAlS9eskvRkjzDwAAAKjsn+qzUaNGeumll1S1alXl5eVp3LhxuuGGGxQSElLin0HzDwAAAJwDevXqpcTERN1///0KDQ1Vp06dNHz48FL9DJp/AAAAQJLK9sS/JGnEiBEaMWLEaT+fU30CAAAAlqD5BwAAACzBsh8AAABAZf+A37OBmX8AAADAEsz8AwAAAGLmHwAAAIAfYeYfAAAAEDP/AAAAAPwIzT8AAABgCZb9AAAAANI5cYXfM8XMPwAAAGAJZv4BAAAAccAvAAAAAD9C8w8AAABYgmU/fqBu1VDTEYxYvvWA6QhGdGtc3XQEI2aNfdV0BCOGXjbRdAQjGkdWNh3BCFvfz4GygmU/AAAAAPwGM/8AAACAmPkHAAAA4EeY+QcAAADEzD8AAAAAP0LzDwAAAFiCZT8AAACAJPn/qh9m/gEAAABbMPMPAAAAiAN+AQAAAPgRmn8AAADAEiz7AQAAAMSyHwAAAAB+hJl/AAAAQMz8AwAAAPAjzPwDAAAAEhf5AgAAAOA/aP4BAAAAS7DsBwAAABAH/AIAAADwI8z8AwAAAGLmHwAAAIAfofkHAAAALMGyHwAAAEAs+wHwX7ZtXK/JQ/tpdN+Oem3UfTqUtM90JDhg3IM9tPClwaZjOCo97agG9e2mlAP7TUdxlK11A7AHzf8Z2r17l/r3uUntLo/Ti5MnqrCw0HQkR9hY9+8Hk7V42kR16T9Ij7++UOFVorRk+iTTsRxh43ifcHG96hp08xV6dPIi01Eck340Vc/GP6RDB+1qgG2t29b9m7rtqrukXC6XkZuTaP7PgNfr1YMPDFGjxo01d/4i7UlM1NKExaZj+ZytdR9O3qdr+g1U0zYdVDE8Qq0791Ry4k7TsXzO1vE+YepTfTX1vc+0N+k301EcM3lsvNp17GI6huNsrNvW/Zu67aobxdH8n4H169bKk+HRyMfiVat2bQ17aLiWLFpoOpbP2Vr3RS3bqHXnnkX3D+//RedXq2kwkTNsHW9JuvvGtmoWE62fk3/XdVc0Ublydrxl3j/yKXXv3d90DMfZWLet+zd121U3iiuzn2T795f9P7vu2rlDzWJjFRISIklqGBOjPYmJhlP5nq11/1leXq7WL5tf7B8D/srW8Q4NCdIzD3TTT78cVs2ocD14WwetnvmIgoP8/zwJ1WpEm45ghI1127p/U7dddZeKy9DNQUaa/z179qh///5q1qyZunfvrrlz5yo/P7/o61lZWbr66qtNRCsVj8ejmjX/+LBwuVxyuwOUnpZmMJXv2Vr3n62eN1NB5UMU16m76Sg+Z+t497y6uUJDgnTdoJc14Y2P1O3+VxVWMUS3dmttOhpw1ti6f1P3cbbUjeKMNP/x8fGKjIzUG2+8of79++utt95S7969tWfPnqLvORcOQHG73QoMCir2WFBwsI5lZxtK5Axb6z7hpx82acOqD3TLQ6PlLuf/s8C2jnfNyHBt3PKzUtOzJEn5+QX6cXeyLqh5vuFkwNlj6/5N3X+woe7S4IBfH/nxxx/19NNPq3Xr1urXr59WrFihyy+/XH369NGKFSsknRvnWQ0LC1Nq6pFij2VlZiowMNBQImfYWrckHUnZrwUvj1ePgY8oMvoC03EcYet4J6WkKqR88Q/J2tUj9MuBI3/xDODcY+v+Td1/sKFuFGek+a9evbo2btxYdD8wMFCPPfaY/vWvf+nZZ5/VhAkTTMQqtcZNmmrL998X3U9OTpLX61VYWJjBVL5na9253hzNmRCvRnHt1CiurXKys5STnXVO/JXqTNg63h+t26qYulEa2LudakaG6/5+V6lZTLQ+/mKb6WjAWWPr/k3dx9lSd2kw8+8jTzzxhJ5++mnNnz+/2ONt27bV/PnztWnTJhOxSq1lqzhleDK0bGmCJGnWmzPU+rI2crvdZoP5mK117968QYeT92nTmuUae0fXotvRwwdNR/MpW8c7NT1LPR+Ypv7Xx+mHhKc1tH8H3fHEW8z8w6/Yun9Td4Ike+pGca5CQ9OWv/76qxITE9W+ffuTvpaTk6N169apU6dOpfqZ2XlnKVwpfLJmteIfG6EKoaEqyM/XzNnvqH79Bs4HcVhZqHv51gOObq+s6Na4uuPbLAvjfV7cUEe3V1Z8u3Ki6QhwUN2qoY5vsyzs3yZQt7m6y5fhw+XqjfjQyHYTX7jOsW0Za/59wUTzL0kpKSnatnWLYpu3UEREhJkQBpium+bfWabHm+YfNjDR/Evm929TqNtM3WW5+a8/0kzz/9Nk55r/MvzrP3dERUUpKirKdAzH2Vq3rRhvwH/Zun9TN2xE8w8AAADo3Djb5Jkqs1f4BQAAAHB20fwDAAAAlmDZDwAAACDJglU/zPwDAAAAtmDmHwAAABAH/AIAAADwI8z8AwAAAGLNPwAAAAA/QvMPAAAAWIJlPwAAAICkgAD/X/fDzD8AAABgCWb+AQAAAHHALwAAAAA/QvMPAAAAWIJlPwAAAIC4wi8AAAAAP8LMPwAAACAO+AUAAADgR5j5BwAAAMSafwAAAAB+hOYfAAAAsATLfgAAAACx7AcAAACAH/Grmf+9hzNNRzCibtVQ0xGM6Na4uukIRrSf/LnpCEZ8u3Ki6QhG2Lp/A4AJFkz8M/MPAAAA2ILmHwAAALCEXy37AQAAAE4XB/wCAAAA8BvM/AMAAADigF8AAAAAfoSZfwAAAECs+QcAAADgR2j+AQAAAEuw7AcAAAAQB/wCAAAA8CPM/AMAAADigF8AAAAAfoTmHwAAALAEzT8AAACg4wf8mridrnvuuUeLFy8u1XNo/gEAAIBzzAcffKD169eX+nkc8AsAAADo3Dng9+jRo5o4caLq1q1b6ufS/AMAAADnkIkTJ6pTp07Kyckp9XNZ9gMAAADI3Jp/r9crj8dT7Ob1ek+Z8euvv9ZXX32lRx999LRqpPkHAAAADJo+fbpatmxZ7DZ9+vSTvi8nJ0fPPPOMxowZo4oVK57Wtlj2AwAAABg0ePBgDRgwoNhjQUFBJ33ftGnT1KRJE7Vv3/60t0XzDwAAAMjcAb9BQUGnbPb/27Jly5SamqpWrVpJkrKzs/Xhhx/qhx9+0JgxY0q0LZp/AAAA4Bzw3nvvKS8vr+j+888/r9jYWN1www0l/hk0/wAAAIDO7IJbTqhWrVqx+xUqVNB5552niIiIEv8MDvg9C9LTjmpQ325KObDfdBTH7N69S/373KR2l8fpxckTVVhYaDqSI2yt+4QpfZrq+qZRpmM4iv3bntc5dVO3DWyt219NmDBBN954Y6meQ/N/htKPpurZ+Id06KA9jYHX69WDDwxRo8aNNXf+Iu1JTNTShNJdWvpcZGvdJ3S5OFKXX1jymQV/wP5tz+ucuqmbumELmv8zNHlsvNp17GI6hqPWr1srT4ZHIx+LV63atTXsoeFasmih6Vg+Z2vdklS5fDk92LGefv49y3QUR7F/2/M6p27qpm5Ixw/4NXFzUplq/jMyMuTxeEzHKJX7Rz6l7r37m47hqF07d6hZbKxCQkIkSQ1jYrQnMdFwKt+ztW5JerBjPX2+6zdt3Z9uOoqj2L/teZ1TN3VTN2xhpPlfuXKlOnbsqFatWmnUqFHyeDwaOnSoLr30Ul166aUaNGiQUlNTTUQrtWo1ok1HcJzH41HNmn/U7XK55HYHKD0tzWAq37O17ha1w9WqTrimfrbHdBTHsX/b8zqn7uOom7ptZ+oKv05yvPlPTU3Vk08+qYceekjvvvuu8vPzde211yozM1OffvqpPvvsM0VERGjs2LFOR0MJud1uBf7XuWiDgoN1LDvbUCJn2Fh3kNulJ65toOc/3q0sb77pOHCAja9zibr/jLr9l611ozjHm/+ff/5ZdevWVc+ePRUTE6Px48crNzdXjz/+uKpVq6bIyEg9/PDDWrdundPRUEJhYWFKTT1S7LGszEwFBgYaSuQMG+u+u20dbT+QoS8Tj/z9N8Mv2Pg6l6j7z6jbf9lad2mw5t8H6tatq6SkJCX+3xqzwMBAzZ49WxdddFHR96xbt05RUXadTvBc0rhJU235/vui+8nJSfJ6vQoLCzOYyvdsrLvzxZG6okEVrXq4rVY93FadL47Uo50b6NHO9U1Hg4/Y+DqXqPsE6qZu+D/Hm//w8HA988wzuu2227R8+XJJKtb4P//885owYYJGjRrldDSUUMtWccrwZGjZ0gRJ0qw3Z6j1ZW3kdrvNBvMxG+se/O5m3Tpzo+54a5PueGuT1u3+XTPW/awZ6342HQ0+YuPrXKJu6qZu2MNVaOjqDhkZGcrMzDzpSmVff/21LrzwQkVGRpb6Z24/kHm24pVar/YtNH3uckVVr+H4tutWDXV8m5+sWa34x0aoQmioCvLzNXP2O6pfv4HjOZxWFupuP/lzR7f3Z6Ovj9G3vxzVii0pjm/7rTtbOb7NE9i/2b/9GXVTt9N1ly/n6OZK5coXvzCy3bXD2zq2LWPNvy+YbP5NMtEcSFJKSoq2bd2i2OYtSnVZ6XOd6bpNNv8mmWz+TWL/dhZ1U7cNTNdN838yJ5v/MvzrR1kXFRVl5bEZttYNu9j6Oqduu1A3/pvTp900oUxd5AsAAACA79D8AwAAAJZg2Q8AAAAgOX7OfROY+QcAAAAswcw/AAAAIA74BQAAAOBHaP4BAAAAS7DsBwAAABAH/AIAAADwI8z8AwAAAOKAXwAAAAB+hJl/AAAAQFKABVP/zPwDAAAAlqD5BwAAACzBsh8AAABAHPALAAAAwI8w8w8AAACIi3wBAAAA8CM0/wAAAIAlWPYDAAAASArw/1U/zPwDAAAAtmDmHwAAABAH/AIAAADwI8z8AwAAALLjIl9+1fzXrRpqOgIctHzrAdMRjPhs5FWmIxhR/a53TUcw4stJvUxHMGLroXTTEYzo1ri66QhG2Pp+but4wyyW/QAAAACW8KuZfwAAAOB0ueT/636Y+QcAAAAswcw/AAAAIC7yBQAAAMCP0PwDAAAAlmDZDwAAACCu8AsAAADAjzDzDwAAAMiOK/wy8w8AAABYgpl/AAAAQFKABVP/zPwDAAAAlqD5BwAAACzBsh8AAABAHPALAAAAwI8w8w8AAACIi3wBAAAA8CM0/wAAAIAlWPYDAAAAiAN+AQAAAPgRZv4BAAAAcYVfAAAAAH6EmX8AAABAkv/P+zPzDwAAAFiDmX+gFLZtXK+Vs19V2m8pqnFhjG66/wlFRtcxHQs+MvGOVhrUOabo/p6UDLUc8YHBRM5JTzuqkYNv07gpMxRVvYbpOI5g/7YL4w1bMfN/hnbv3qX+fW5Su8vj9OLkiSosLDQdyRE21v37wWQtnjZRXfoP0uOvL1R4lSgtmT7JdCxH2DjektT8ggj1mfSp6gxaoDqDFuiqJ1eajuSI9KOpejb+IR06uN90FMewf9u1fzPedo13abhcLiM3J9H8nwGv16sHHxiiRo0ba+78RdqTmKilCYtNx/I5W+s+nLxP1/QbqKZtOqhieIRad+6p5MSdpmP5nK3j7Q5w6aJa4fpyxyGlZ+UqPStXnuw807EcMXlsvNp17GI6hqPYv+3avxlvu8YbxdH8n4H169bKk+HRyMfiVat2bQ17aLiWLFpoOpbP2Vr3RS3bqHXnnkX3D+//RedXq2kwkTNsHe/GtcPlkrT2n121f9Ytev+xDoo+v4LpWI64f+RT6t67v+kYjmL/tmv/ZrztGu/SCHCZuTlao7Ob+9/Gjh2ro0ePmo5RYrt27lCz2FiFhIRIkhrGxGhPYqLhVL5na91/lpeXq/XL5hf78PBXto53TI0w7UhO08BX1+uyx5crL69AU+5ubTqWI6rViDYdwSj2b//fv/+M8bZrvGHggN+EhIS//NqSJUtUq1YtnXfeeerVq5djmU6Xx+NRzZp/fEi6XC653QFKT0tT5bAwg8l8y9a6/2z1vJkKKh+iuE7dTUfxOVvH+/0vf9b7X/5cdP+x2Zv03ZQeqhRSThnH7Fj+Yyv2b//fv/+M8bZrvGGg+V++fLnWr1+vCy+8ULGxscW+lpubq++++06hoaHnRPPvdrsVGBRU7LGg4GAdy872653I1rpP+OmHTdqw6gMNeXaa3OX8/4RZto/3CWlZXrkDAhQVHqKMYxmm48BH2L/t2r8Zb7vGuyScPvjWBMdf6W+++aY++OADPf/88ypfvrxGjhyp0NBQSdLq1av1xBNPqEaNc+O0cmFhYfrpp93FHsvKzFRgYKChRM6wtW5JOpKyXwteHq8eAx9RZPQFpuM4wtbxHn9rC2366TclfPOLJOmSC89XfkGBkn/PMpwMvsL+fZwN+7fEeJ9gy3jjD0bW/Pfo0UMrVqxQdna2unfvrrVr15qIccYaN2mqLd9/X3Q/OTlJXq9XYX7+r2db68715mjOhHg1imunRnFtlZOdpZzsLL8/TZqt471lX6qeujlWbWIidcXFUZp4Ryu9t3aPjnnzTUeDD7B/H2fL/s14H2fLeJeGy2Xm5iRjf+MKCwvTc889p6+//lrPPPOMli1bpvz8c+tDtWWrOGV4MrRsaYK69+ylWW/OUOvL2sjtdpuO5lO21r178wYdTt6nw8n7tGnN8qLHR06dq/MiqxtM5lu2jvf89XsVUyNM7w6/Up5jeVr+n181bsFm07HgI+zfdu3fjLdd443iXIVl4J+5Xq9Xr776qpYvX6733ntPUVFRp/VzTJyC+5M1qxX/2AhVCA1VQX6+Zs5+R/XrN3A+iMPKQt3Ltx5wdHtlRbfGzn8wlYXxrn7Xu45ur6z4clIv0xGM2Hoo3XQEI2zdv3k/d05ZGO/yZfjwijve+8HIduf0b+bYtspE83+2mLr+TkpKirZt3aLY5i0UERFhJoQBpuvmw8JZpseb5t8uNP/OMr1/837uLNPjTfN/Mieb/xL/+vPy8jRmzBiNHz++6LEjR45o1KhRev3114t9r8fjUcWKFc9eyjIuKirqtP9acS6ztW5bMd6A/2L/tgvjbbcSH/Bbrlw5rV+/vtjBMLt37z5pnVh+fr4uu+yys5cQAAAAcABX+P0vaWlp6tSpk7755htJ0ueff66uXbvq4MGD+vXXXyX93zlkOWUUAAAAUOaUaNnPsWPHtGjRIoWGhmrWrFmqU6eOjh07ps8//1x79uyRx+PR+PHj1alTJ/Xu3VtB/3UBCQAAAKCss+EiXyWa+T948KDmzJmjY8eOKS/v+FG1s2fP1o033qhvvvlGt9xyi1avXq1LLrlEEydOVG5urk9DAwAAACi9EjX/devW1Ycffqhhw4bpzjvv1GeffaZPPvlEt99+u4KDgyUdP3ikd+/eWrhwYdFjAAAAAMqOEq/5T05O1oEDBzRr1iw9/vjjuvfee4uW92zYsEG9e/fWlVdeqR07dvgsLAAAAOArLkM3J5W4+c/NzdXmzZtVvXp1jRw5Uv/85z919OhRFRYWqlatWnryySe1YcMGNWvm3HlKAQAAAJRcic/zn5aWpl27dunKK6/U6tWrtXfvXk2YMEFZWVmqXr26IiMj9fHHH6tevXq+zAsAAAD4RAAH/B63ceNG3XvvvYqOjta6det0/vnn68EHH9QXX3yhBx54QLm5ueratatWrlwpr9fr68wAAAAATkOJmv8WLVpo1KhR+u233/T2229LksqXL68+ffooKSlJgYGBmjt3rl5++WU1btxY+fn5vswMAAAAnHUul5mbk0rU/Lvdbt10001asGCBFi1apA8++ECSdM0112jFihXyeDyKiIiQJBUWFiozM9N3iQEAAACclhKv+ZekWrVq6aWXXlKtWrUkSRdddJEefPBBlS9fvuh7XC6Xtm7denZTAgAAADhjpWr+JSk2NrbY/QEDBhT9t8fjUcWKFc88FQAAAOAwrvD7JwUFBdq4cWPRf1911VVFX8vJydGLL76ojh07KiUl5eynBAAAAHDGStX8n5jlDwgIkMfjkSRt3rxZ119/vdasWaPRo0eratWqvkkKAAAA+JANB/yWeNlPuXLliq3tP3F13/DwcPXp00f33HOP3G732U8IAAAA4Kwo1Zr/wMDAov/2er168cUXi+7/61//kiQFBwerR48eRQcFAwAAACgbStX8FxYWFv23y+VSSEjISd+zadMm/fDDD5o+ffqZpwMAAAAcYsMVfkt9tp8TAgMDdd999yklJUU7duwoOgB49erVevPNN89aQAAAAADFpaamau/evbrggguKrrdVEiVu/r1eb7GLd+Xl5UmStm7dqvj4eFWtWlV33XWXunbtqk6dOpUiOgAAAGDeuTLxv2LFCo0ZM0Y1a9bU3r179c9//lPXX399iZ5bqgN+Z8yYIY/Ho8TERNWoUUOFhYUKDQ3VqlWr9NVXX2nq1Kk6cuSIBg0adNrFAAAAADi19PR0jRs3Tu+++64aNmyohIQETZ48+ew3/wEBAbr88sv1888/6/3339e7776roUOH6vvvv9eUKVPUpUsXderUSTk5OaddDAAAAIC/lpmZqVGjRqlhw4aSpIsuukhpaWklfn6Jm//JkyerfPnyysjI0M6dO/X2229r9+7d6tWrl7755ht98803ko5fDyA3N1cjRowoZSkAAACAOefCFX6rV6+uHj16SJJyc3M1a9Ysde7cucTPL9Wyn8DAQAUGBio5OVlvv/22PB6PVq1ape7duxed9z8vL08FBQWlLAMAAACwk9frldfrLfZYUFBQUX99Kjt27NAdd9yhwMBAffjhhyXelqvwz+fvLIF9+/Zp5syZ+sc//qEvv/xS//73v7V9+3aNGzdOV155ZWl+1FmXnWd083DY3sOZf/9Nfqhu1VDTEYwYsWy76QhGDL2stukIcJCt+zfv53Ypf9rnmvS9YUvMfNY0TFqtqVOnFnts6NChGjZs2F8+p7CwUNu3b9fEiRNVsWJFvfrqqyXaVql//ZUqVdKll14ql8ultm3bqm3btvr00081d+5ctWvXTgEBAaX9kQAAAIC1Bg8erAEDBhR77H/N+kvHlyhdfPHFmjBhgjp06KC0tDSFhYX97bZK3fxHRESoW7duxR7r0KGDOnToUNofBQAAAJQZptb8/90Snz/76quvtHbtWj3++OOSJLfbLUklnoAv1TT90qVL9f777+uHH34o9nh2dnax0wt9+eWX+v7770vzowEAAAD8jQsvvFDz58/X/PnzdeDAAb3wwgtq27atKlWqVKLnl6r5/9e//qX33ntPP/74Y7HHy5Urp5SUFEnHz/Yzfvz4orP/AAAAADg7oqKi9K9//UuzZ8/W9ddfr2PHjmnSpEklfn6pl/0sWbLk5B/yf2cCkqRFixbJ7XbrnnvuKe2PBgAAAIwJKPtn+pQkXXHFFbriiitO67mlav7/bh1UTk6OXn75ZU2ePLlo/REAAACAsqHUM/8ej0f33HOPoqOjFRUVperVq6t69eqSpJ9++kkXXXSRWrdufdaDAgAAAL50rsz8n4kSNf9HjhzRwoULdeTIEXk8Hl111VVyuVzKzs7Wjh07tHbtWqWmpmr27NmaMmWKrzMDAAAAOA0lav4nTZqkb7/9VpJUrVo13X///frggw8UHR2tFi1aSJIuvfRShYSEaMiQIZo9ezbLfgAAAIAypkRn+4mPj9dHH32k8847T5L0888/a9y4cSooKCj6HrfbrX/84x+qUKGC3njjDd+kBQAAAHzE5XIZuTmpRM1/5cqVi4V78sknNXjwYLVq1UrDhw/X4sWLi753xIgReuutt5Sdne2bxAAAAABOS6nO83/Ck08+qQEDBuidd97Rhg0b1KZNGxUWFkqSYmJiVKNGDX366adnNSgAAADgSwEuMzdHayzNNxcWFuq5557T3r175XK5tHz5cv3zn/9UtWrVipp/Sbr66qu1atWqsx4WAAAAwOkr1ak+r7/+enk8HuXk5CggIEDz5s2TJHm9Xnm93qLva9GihWJjY89uUgAAAMCHHF5+b0Spmv8RI0ac8vHAwEC9//772rVrlxo2bKg2bdqclXAAAAAAzp4SL/vJy8vT6tWrT/k1l8ulOnXqqF+/fpKk7777Tjk5OWcnIQAAAICzosTNf2FhoWbMmPGXXw8MDFS5csf/kPDMM89o+fLlZ54OAAAAcEiAy2Xk5qQSL/s50dx//vnneuaZZxQcHHzS9wQEBGjTpk3yeDzq2bPnWQ0KAAAA4MyU+lSfOTk56tixowoKCtS1a1fFxcWpsLBQzz//vAoLC/Xhhx9q4MCBRX8FAAAAAM4FAYZuTjqtDr1atWoqX7686tSpI7fbreDg4KKz+8TExOjaa689qyEBAAAAnLkSNf+FhYWaPn26srKydPDgwf/5vX369DkrwQAAAACcXSX6S0Nubq527NihPXv2aNq0ab7OBAAAADjO5TJzc1KJmv+goCC99NJLatKkif7xj3/8z++dOXOmkpKSzko4AAAAAGdPqY8xcLlcOnr0qHJzc/X7778X/fevv/4qScrMzNSsWbPOelAAMCE0yK26ESEKDXKbjgIA8DEbTvV5WgcYz5kzRykpKZo6daoWLVqklJQU9ejRQy6XS3fffbfWrFmjrKyss50VKBPS045qUN9uSjmw33QU+FjLmpU15pp6uiW2msZ3qa+WNSubjuQYW1/nttZtK8YbNipx819QUKC8vDx17txZP/74o7777ruTbgUFBapYsaK6dOmilStX+jJ3mbF79y7173OT2l0epxcnT1RhYaHpSI6wte70o6l6Nv4hHTpo1weFjeMdEhigm2Oj9OLafZrw6V7N3XxAvZpEmo7lCFtf57bWbeP+LTHeto13SbHm/0/y8vJ0ySWX/OXXvV6v8vPzJUm9e/dW8+bNzzhcWef1evXgA0PUqHFjzZ2/SHsSE7U0YbHpWD5na92SNHlsvNp17GI6hqNsHe/gcgFa9EOKDmTkSJKS0nIUEuj02ZjNsPF1LtlZt637t8R42zbe+EOJP8mCgoIUHx//l193u916+eWXJUkNGzZU/fr1SxwiMzNTP//8s3Jyckr8nLJg/bq18mR4NPKxeNWqXVvDHhquJYsWmo7lc7bWLUn3j3xK3Xv3Nx3DUbaO99FjedqYlC5JCnBJnRpE6Pv9GYZTOcPG17lkZ9227t8S423beOMPZ20ay+12q02bNn/7fV26dCk6HuDAgQO69957FRcXp2uvvVYtWrTQ6NGjlZ2dfbZi+dSunTvULDZWISEhkqSGMTHak5hoOJXv2Vq3JFWrEW06guNsHm9Jqlk5WBO6NlSjyIpauCXFdBxH2Pg6l+ys2+b9m/G2a7xLKsBl5uZojc5uTtq3b58KCgokSaNHj5bb7dbq1au1adMmvfHGG/rPf/6jKVOmOB3rtHg8HtWs+cebh8vlktsdoPS0NIOpfM/Wum1l+3gnp+fo5fX7dCA9R7e1qGE6DnBW2b5/24bxhmSg+Xf96aiGjRs3avTo0apRo4YqVqyoNm3aaPTo0Vq1apXTsU6L2+1WYFBQsceCgoN17Bz5y8XpsrVuWzHex9f7z/l2v5pVr2jNun/Ygf3bLoz33+NUnz5QWFio7777TseOHVNUVJSOHj1a7OuBgYHKzMx0OtZpCQsLU2rqkWKPZWVmKjAw0FAiZ9hat61sHe+GVSrohj+d3ef//mApTowBf2Lr/m0rxhuSgeb/1ltv1auvvqp27drpt99+07hx44q+9uGHH+rJJ59U3759nY51Who3aaot339fdD85OUler1dhYWEGU/merXXbytbxTvF41faCcLW9IFzhIeXUs0lV7TiUqey8AtPRgLPG1v3bVow3JAPN/+jRozVv3jxt2LBB77zzjm699dair33//fe677779Mgjjzgd67S0bBWnDE+Gli1NkCTNenOGWl/WRm63f18J1Na6bWXreKdl52nmhmR1qBehp66+UEHuAM3eZNf5wOH/bN2/bcV4/z0bzvPvKvSjqztk5zm/zU/WrFb8YyNUITRUBfn5mjn7HdWv38D5IA4rC3XvPXxuLA872+pWDXV8m2VhvEcs2+7o9sqKoZfVNh0BDrJ1/+b93DllYbzLl3N0c6UybvVPRrY7ulPJT5F/pmj+z4KUlBRt27pFsc1bKCIiwkwIA0zXzYeFs0yPN80/bGDr/s37ubNMj3dZbv6fXWOm+X/yauea/zL86z93REVFKSoqynQMx9lat60Yb8B/sX/bhfG2G80/AAAAIMklhxfgG8AJqwEAAABL0PwDAAAAlmDZDwAAACApwP9X/TDzDwAAANiCmX8AAABAzPwDAAAA8CM0/wAAAIAlWPYDAAAASHK5/H/dDzP/AAAAgCWY+QcAAADEAb8AAAAA/Agz/wAAAIAkC5b8M/MPAAAA2ILmHwAAALAEy34AAAAASQEWrPth5h8AAACwBDP/AAAAgDjVJwAAAAA/QvMPAAAAWIJlPwAAAIA4zz8AAAAAP8LMPwAAACApQP4/9c/MPwAAAGAJv5r533s403QEI+pWDTUdwQhb67ZVhwvDTUcwwtbX+Yhl201HMGLoZbVNRzDC1tc5yh7W/AMAAADwGzT/AAAAgCX8atkPAAAAcLq4wi8AAAAAv8HMPwAAACApwIIjfpn5BwAAACxB8w8AAABYgmU/AAAAgDjPPwAAAAA/wsw/AAAAIA74BQAAAOBHaP4BAAAAS7DsBwAAABAH/AIAAADwI8z8AwAAALJjVtyGGgEAAACImX8AAABAkuSyYNE/M/8AAACAJWj+AQAAAEuw7AcAAACQ5P+Lfpj5BwAAAKzBzD8AAAAgKYADfgEAAAD4C5p/AAD+T2iQW3UjQhQa5DYdBQB8gub/LEhPO6pBfbsp5cB+01Ecs3v3LvXvc5PaXR6nFydPVGFhoelIjqBuu+retnG9Jg/tp9F9O+q1UffpUNI+05EcYet4t6xZWWOuqadbYqtpfJf6almzsulIjuFzzJ7Xua11l5TL0M1JNP9nKP1oqp6Nf0iHDtrzhun1evXgA0PUqHFjzZ2/SHsSE7U0YbHpWD5H3XbV/fvBZC2eNlFd+g/S468vVHiVKC2ZPsl0LJ+zdbxDAgN0c2yUXly7TxM+3au5mw+oV5NI07EcweeYPa9zW+tGcTT/Z2jy2Hi169jFdAxHrV+3Vp4Mj0Y+Fq9atWtr2EPDtWTRQtOxfI667ar7cPI+XdNvoJq26aCK4RFq3bmnkhN3mo7lc7aOd3C5AC36IUUHMnIkSUlpOQoJtOMjks8xe17nttZdGi6XmZuTysTZfrxer5YuXaqkpCTVqFFDV1xxhWrUqGE6VoncP/IpVasRrZlTJ5uO4phdO3eoWWysQkJCJEkNY2K0JzHRcCrfo2676r6oZZti9w/v/0XnV6tpKI1zbB3vo8fytDEpXZIU4JI6NYjQ9/szDKdyBp9j9rzOba0bxRmf1sjLy9Mdd9yht956S7/88osWL16s6667Tp988onpaCVSrUa06QiO83g8qlnzj7pdLpfc7gClp6UZTOV71H2cLXX/WV5ertYvm6/WnXuajuJzto93zcrBmtC1oRpFVtTCLSmm4ziCzzF7Xue21l0aLpfLyM1JRpr/++67T0lJSZKk9evXKzg4WMuWLdOUKVM0f/58jRgxQs8995yJaCgBt9utwKCgYo8FBQfrWHa2oUTOoO4/2FD3n62eN1NB5UMU16m76Sg+Z/t4J6fn6OX1+3QgPUe3tTg3/gKN0rP1dW5r3SjOSPNfr1499erVS1OnTlVqaqouu+wyud1/nFatS5cu+u2330xEQwmEhYUpNfVIsceyMjMVGBhoKJEzqPsPNtR9wk8/bNKGVR/olodGy12uTKyU9Cnbx1s6vt5/zrf71ax6RWvW/dvG1te5rXWjOCPvaiNHjtR7772nL774QmPHjtXHH3+sjIw/1lZ+9tlnuuiii0xEQwk0btJUW77/vuh+cnKSvF6vwsLCDKbyPeo+zpa6JelIyn4teHm8egx8RJHRF5iO4whbx7thlQq64U9n9ykoOP7/nAXRP9n6Ore17tIIMHRzkrEpjYYNG2ru3Ll64oknlJKSoiuvvFJ33XWX+vbtqxdeeEHx8fGmouFvtGwVpwxPhpYtTZAkzXpzhlpf1qbYX2/8EXUnSLKn7lxvjuZMiFejuHZqFNdWOdlZysnO8vtzYts63iker9peEK62F4QrPKScejapqh2HMpWdV2A6GnzA1te5rXWjOFdhGfgky8rK0scff6yUlBRVq1ZN7du3P61/hW4/kOmDdCXTq30LTZ+7XFHVnV8jWrdqqOPb/GTNasU/NkIVQkNVkJ+vmbPfUf36DRzP4TTqNlf38q0HHN3etg3r9O7k0Sc9PnLqXJ0XWd2xHN0aO7etE8rCeI9Ytt3R7UlSo8hQ3dQ0SuEh5bT9UKbmbz4ojzff0QxDL6vt6Pb+jM8x3s+dUr4Mr6BcsNnM9S76NHduvysTzf/ZYrL5N8nEm6YkpaSkaNvWLYpt3kIRERFGMphA3Wbqdrr5LytMNP+S+fE20fyXBSabf5P4HHOW6bpp/k/mZPNfhn/9KOuioqIUFRVlOobjqBs2YLxhA1tf57bW7U9Wr16t5557TgcOHFDjxo01YcIE1atXr0TP5TQGAAAAgCSXoVtp/PLLLxo1apRGjBihtWvXqkaNGnryySdL/HyafwAAAOAckZiYqEceeURdu3ZVlSpV1K9fP/34448lfj7LfgAAAADJ8avtno4OHToUu793717VqVOnxM+n+QcAAAAM8nq98nq9xR4LCgpS0H9dkflUz5s1a5buuuuuEm+L5h8AAACQufXw06dP19SpU4s9NnToUA0bNux/Pu+ll15ShQoV1KdPnxJvi+YfAAAAMGjw4MEaMGBAscf+btb/iy++0Lx587RgwQIFBgaWeFs0/wAAAIBBJVni82e//vqrRo4cqTFjxqh+/fql2hbNPwAAAKBz44Df7OxsDR48WJ06ddLVV1+tzMzjF7mtUKFCifLT/AMAAADniPXr1ysxMVGJiYlasGBB0eNr1qxRdHT03z6f5h8AAABQ6S+4ZUKnTp20c+fO034+F/kCAAAALEHzDwAAAFiCZT8AAACApHPgeN8zxsw/AAAAYAlm/gEAAABJAefEIb9nhpl/AAAAwBLM/AMAAABizT8AAAAAP0LzDwAAAFiCZT8AAACAJBcH/AIAAADwF8z8AwAAAOKAXwAAAAB+hOYfAAAAsATLfoBzzN7DmaYjGDH5w12mI8BBHS4MNx3BiLpVQ01HAKzGFX4BAAAA+A1m/gEAAABxwC8AAAAAP8LMPwAAACBm/gEAAAD4EZp/AAAAwBIs+wEAAAAkuTjVJwAAAAB/wcw/AAAAICnA/yf+mfkHAAAAbEHzDwAAAFiCZT8AAACAOOAXAAAAgB9h5h8AAAAQV/gFAAAA4EeY+QcAAADEmn8AAAAAfoTmHwAAALAEy34AAAAAcYVfAAAAAH6EmX8AAABAHPALAAAAwI/Q/AMAAACWYNkPAAAAIK7wixJKTzuqQX27KeXAftNRAJ+x+XU+pU9TXd80ynQMx2zbuF6Th/bT6L4d9dqo+3QoaZ/pSI6wtW4AdqH5P0PpR1P1bPxDOnTQroZo9+5d6t/nJrW7PE4vTp6owsJC05EcYWvdtr7OJanLxZG6/MII0zEc8/vBZC2eNlFd+g/S468vVHiVKC2ZPsl0LJ+ztW7J3vc16rar7pJyGbo5ieb/DE0eG692HbuYjuEor9erBx8YokaNG2vu/EXak5iopQmLTcfyOVvrlux8nUtS5fLl9GDHevr59yzTURxzOHmfruk3UE3bdFDF8Ai17txTyYk7TcfyOVvrtvV9jbrtqhvF0fyfoftHPqXuvfubjuGo9evWypPh0cjH4lWrdm0Ne2i4lixaaDqWz9lat2Tn61ySHuxYT5/v+k1b96ebjuKYi1q2UevOPYvuH97/i86vVtNgImfYWret72vUbVfdKM5I85+QkKCkpCQTmz7rqtWINh3Bcbt27lCz2FiFhIRIkhrGxGhPYqLhVL5na92Sna/zFrXD1apOuKZ+tsd0FGPy8nK1ftn8Yk2xDWyq29b3Neq2q+7SCHC5jNwcrdHRrf2fJ554Qn369NEzzzyj/fvtW0N8rvN4PKpZ849m0OVyye0OUHpamsFUvmdr3TYKcrv0xLUN9PzHu5XlzTcdx5jV82YqqHyI4jp1Nx3FUTbVbev7GnUfZ0vdKM7Ysp8lS5aoevXquvnmm/XAAw9o3bp1ys+390P2XOJ2uxUYFFTssaDgYB3LzjaUyBm21m2ju9vW0fYDGfoy8YjpKMb89MMmbVj1gW55aLTc5ew5K7Rtddv6vkbdf7Ch7tLggF8fCg0N1ZAhQ/Tpp5+qY8eOevnll9WmTRs98cQTWrJkifbu3WsqGv5GWFiYUlOLN0VZmZkKDAw0lMgZttZto84XR+qKBlW06uG2WvVwW3W+OFKPdm6gRzvXNx3NEUdS9mvBy+PVY+Ajioy+wHQcx9hYt63va9T9BxvqRnFGpjVcf1rbFBQUpJtuukk33XST9u3bp48++kiLFi3SM888ox9++MFEPPyNxk2aFjtAKDk5SV6vV2FhYQZT+Z6tddto8LubVS7gj/epYR3q6cf96Vqx5aDBVM7I9eZozoR4NYprp0ZxbZWTffxMR0HBIcXeu/2NrXXb+r5G3cfZUnep+O/uXsRI8/9X55StU6eOBg8erMGDBysnJ8fhVCiplq3ilOHJ0LKlCeres5dmvTlDrS9rI7fbbTqaT9lat40OZ3iL3T+Wm6+0Y7lKO5ZnKJFzdm/eoMPJ+3Q4eZ82rVle9PjIqXN1XmR1g8l8y9a6bX1fo2676kZxrkIDV3dYsmSJunfvrnJneT3l9gOZZ/XnnSvqVg11fJufrFmt+MdGqEJoqAry8zVz9juqX7+B4zmcVhbq3nvYztf5gNmbTEcwYuR1DU1HgIO6NXb+Hxpl4X3NBOo2V3f5Mnw4zdeJR41s97J64Y5ty0jz7ys0/85KSUnRtq1bFNu8hSIi7LkCqum6af7tQvNvFxPNv2T+fc0U6jZTd1lu/r9JNHPmo9b1nFt6VYZ//SjroqKiFBUVZTqG42ytG4D/svV9jbphI5p/AAAAQJIfH99fxNipPgEAAAA4i+YfAAAAsATLfgAAAABZcZp/Zv4BAAAAWzDzDwAAAEhWTP0z8w8AAABYgpl/AAAAQJLLgql/Zv4BAAAAS9D8AwAAAJZg2Q8AAAAgrvALAAAAwI8w8w8AAADIijN9MvMPAAAA2ILmHwAAALAEy34AAAAAyYp1P8z8AwAAAJZg5h8AAAAQV/gFAAAA4EeY+QcAAADERb4AAAAA+BGafwAAAMASLPsBAAAAZMWZPpn5BwAAAGzhVzP/A2ZvMh3BiM9GXmU6ghHLtx4wHcGIbo2rm45gxMjrGpqOYISt4733cKbpCHBQ+8mfm45ghK2f32WaBVP/zPwDAAAAlqD5BwAAACzhV8t+AAAAgNPFFX4BAAAA+A1m/gEAAABxhV8AAAAAfoSZfwAAAEBWnOmTmX8AAADAFjT/AAAAgCVY9gMAAABIVqz7YeYfAAAAsAQz/wAAAIC4yBcAAAAAP0LzDwAAAFiC5h8AAADQ8Sv8mriVVmpqqjp27KikpKRSP5fmHwAAADhHHDlyREOGDFFycvJpPZ/mHwAAANDxM32auJXG8OHD1bVr19MtkeYfAAAAOFeMGzdOd95552k/n1N9AgAAAJKxi3x5vV55vd5ijwUFBSkoKOik761Vq9YZbYuZfwAAAMCg6dOnq2XLlsVu06dP98m2mPkHAAAADBo8eLAGDBhQ7LFTzfqfDTT/AAAAgMxd4fevlvj4Ast+AAAAAEsw8w8AAADo9C64da5h5v8smdKnqa5vGmU6hmN2796l/n1uUrvL4/Ti5IkqLCw0HckR2zau1+Sh/TS6b0e9Nuo+HUraZzqSIxhvxtsW6WlHNahvN6Uc2G86imNsHm+Jz2/bxtuf7Ny5U9HR0aV+Hs3/WdDl4khdfmGE6RiO8Xq9evCBIWrUuLHmzl+kPYmJWpqw2HQsn/v9YLIWT5uoLv0H6fHXFyq8SpSWTJ9kOpbPMd6Mtw3jLUnpR1P1bPxDOnTQnsbf5vGW+Py2bbxxHM3/Gapcvpwe7FhPP/+eZTqKY9avWytPhkcjH4tXrdq1Neyh4VqyaKHpWD53OHmfruk3UE3bdFDF8Ai17txTyYk7TcfyOcab8bZhvCVp8th4tevYxXQMR9k83nx+2zXeJXUuXOH3TJWpNf87duxQQUGBYmJi5Ha7TccpkQc71tPnu35TcKA9/47atXOHmsXGKiQkRJLUMCZGexITDafyvYtatil2//D+X3R+tZqG0jiH8T6O8fZ/9498StVqRGvm1MmmozjG5vHm89uu8cYfjLzik5OTdfvtt+vKK69UfHy8UlNT1bt3b915553q27evrrnmGm3fvt1EtFJpUTtcreqEa+pne0xHcZTH41HNmn+sMXO5XHK7A5SelmYwlbPy8nK1ftl8te7c03QUn2O8GW9bxrtajdKvnT3X2TrefH4fZ8t4l4oFU/9Gmv/Ro0erdu3amjJlirxer/r06aO2bdvqm2++0Zdffqno6Gg99dRTJqKVWJDbpSeubaDnP96tLG++6TiOcrvdCvyvc9EGBQfrWHa2oUTOWz1vpoLKhyiuU3fTUXyO8Wa8bRtvm9g43nx+2zXeOJmR5v+7777TsGHD1LJlS40ZM0ZJSUkaMmSIJKlixYrq06ePdu3aZSJaid3dto62H8jQl4lHTEdxXFhYmFJTi9edlZmpwMBAQ4mc9dMPm7Rh1Qe65aHRcpcrUyvnfILxZrxtGm/b2DjefH7bNd6l5TL0PycZ+SQLDw/X0aNHVa1aNSUlJamwsFCJiYlq0qSJJOm3335T1apVTUQrsc4XRyq8QpBWPdxWklQ+MEBXX1RVF1evpEkf/2Q4nW81btK02AFCyclJ8nq9CgsLM5jKGUdS9mvBy+PVY+Ajioy+wHQcRzDejLct420jG8ebz2+7xhsnM9L8P/TQQ7rvvvvUvHlzff3117rlllv0wAMPqGvXrvr999+1atUqDRw40ES0Ehv87maVC/jjX2rDOtTTj/vTtWLLQYOpnNGyVZwyPBlatjRB3Xv20qw3Z6j1ZW3OmYO0T1euN0dzJsSrUVw7NYprq5zs42eICAoOkcuPrwrCeDPeNoy3rWwcbz6/7RpvnMxI89+rVy+1bNlS27Zt09ChQ1WvXj1t3rxZH374oSpXrqxnn31WXbt2NRGtxA5neIvdP5abr7RjuUo7lmcokXPKlSunp8eMU/xjI/TiC8+rID9fM2e/YzqWz+3evEGHk/fpcPI+bVqzvOjxkVPn6rzI6gaT+RbjzXjbMN62snG8+fy2a7xLy4/ndoq4Cv3o0m6XTfjcdAQjPht5lZHtpqSkaNvWLYpt3kIREc5fJGX51gOOb7Ms6NbYTOPJeJth63jvPZzp+DbLgrpVQ41s1/R4t5/M57eTTI93+TJ8+NRPh44Z2W79yBDHtlWGf/0o66KiohQVZc8l0W3HeNuF8bYL420XxvuvWTDxzxV+AQAAAFvQ/AMAAACWYNkPAAAAIFmx7oeZfwAAAMASzPwDAAAAkuNX2zWBmX8AAADAEjT/AAAAgCVY9gMAAADIjiv8MvMPAAAAWIKZfwAAAEBWnOmTmX8AAADAFsz8AwAAAJIVU//M/AMAAACWoPkHAAAALMGyHwAAAEBc4RcAAACAH2HmHwAAABAX+QIAAADgR2j+AQAAAEuw7AcAAACQFaf5Z+YfAAAAsAUz/wAAAIA44BcAAACAH2HmHwAAAJBkw6p/v2r+R17X0HQEOKhxZGXTEYzYezjTdATA5+pWDTUdAQ6y9fN7+dYDpiMY0Tu2uukIVmPZDwAAAGAJv5r5BwAAAE4XB/wCAAAA8BvM/AMAAACy4XBfZv4BAAAAa9D8AwAAAJZg2Q8AAAAgDvgFAAAA4EeY+QcAAAAkuSw45JeZfwAAAMASzPwDAAAAkhXn+mTmHwAAALAEzT8AAABgCZb9AAAAALJi1Q8z/wAAAIAtmPkHAAAAxEW+AAAAAPgRmn8AAADAEiz7AQAAAMQVfgEAAAD4EWb+AQAAAMmKc30y8w8AAABYgpl/AAAAQFZM/DPzDwAAANiC5v8Mbdu4XpOH9tPovh312qj7dChpn+lI8LH0tKMa1LebUg7sNx3FUTbWzf4N+C9b929b68YfaP7PwO8Hk7V42kR16T9Ij7++UOFVorRk+iTTsRyxe/cu9e9zk9pdHqcXJ09UYWGh6UiOSD+aqmfjH9Khg/Y0wJKddbN/27d/U7c9ddu6f9tad2m4XGZuTqL5PwOHk/fpmn4D1bRNB1UMj1Drzj2VnLjTdCyf83q9evCBIWrUuLHmzl+kPYmJWpqw2HQsR0weG692HbuYjuE4G+tm/7Zr/6Zuu+q2df+2tW4UR/N/Bi5q2UatO/csun94/y86v1pNg4mcsX7dWnkyPBr5WLxq1a6tYQ8N15JFC03HcsT9I59S9979TcdwnI11s3/btX9Tt11127p/21p3abgM/c9Jxpr/tLQ0eTweU5s/6/LycrV+2fxiO5W/2rVzh5rFxiokJESS1DAmRnsSEw2ncka1GtGmIxhha90nsH/7//5N3XbV/Wc27d9/ZmvdMND8p6am6tZbb9Vll12muLg43XLLLVq2bNk5v8Zw9byZCioforhO3U1H8TmPx6OaNf9oBl0ul9zuAKWnpRlMBfgO+7f/79/UfZwtdf+ZTfv3n9laNww0/+PHj1fFihW1fPlyrVixQnFxcXr00UfVrVs3ffLJJ07HOSt++mGTNqz6QLc8NFrucv5/6QS3263AoKBijwUFB+tYdrahRIDvsH/bsX9T9x9sqPsE2/bvE2ytuyQ44NcH1q1bpzFjxqhevXq68MILNXLkSDVv3lytWrXSU089pdtvv11bt251OtZpO5KyXwteHq8eAx9RZPQFpuM4IiwsTKmpR4o9lpWZqcDAQEOJAN9g/z7Ohv2buv9gQ92Snfu3ZG/d+IPjzX9UVJR+/vnnYo95vV4NHDhQn3zyia644grdc889Tsc6LbneHM2ZEK9Gce3UKK6tcrKzlJOddc4vYfo7jZs01Zbvvy+6n5ycJK/Xq7CwMIOpgLOL/fs4W/Zv6j7Olrpt3b9trRvFOf63nrvuuksPPPCA+vTpo+rVq+uTTz5R+fLlVatWLUnSoEGD1K9fP6djnZbdmzfocPI+HU7ep01rlhc9PnLqXJ0XWd1gMt9q2SpOGZ4MLVuaoO49e2nWmzPU+rI2crvdpqMBZw37t137N3XbVbet+7etdaM4V6GBf+59+eWXWrp0qVJTU9WoUSPdfffdZ2WWYeH3B85CunNPt8bO77CfrFmt+MdGqEJoqAry8zVz9juqX7+Boxn2Hs50dHswa+uhdNMRjLB1/zaBus3VvXyrnZ/ftuodW3b/oXH0WL6R7YaHOPcPbiPNv6/Q/DsrJSVF27ZuUWzzFoqIiHB8+zT/dqH5d5bp/dsU6jZTN82/XWj+T+Zk888h3jhtUVFRioqKMh0DgA/Yun9TNwB/R/MPAAAASI5fbdcEY1f4BQAAAOAsZv4BAAAAOX/BLROY+QcAAAAsQfMPAAAAWIJlPwAAAIBkweG+zPwDAAAA1mDmHwAAAJCsmPpn5h8AAACwBDP/AAAAgLjIFwAAAAA/QvMPAAAAWIJlPwAAAIC4wi8AAAAAP8LMPwAAACArzvTJzD8AAABgC5p/AAAAwBIs+wEAAAAkK9b9MPMPAAAAWIKZfwAAAEBc4RcAAABAGbNr1y7ddNNNiouL08SJE1VYWFji59L8AwAAAOcIr9erIUOGqHHjxlq0aJESExO1ePHiEj+f5h8AAADQ8Sv8mriVxtq1a+XxeBQfH6/atWtr+PDhWrhwYYmfz5p/AAAAwCCv1yuv11vssaCgIAUFBZ30vTt27FBsbKxCQkIkSTExMUpMTCzxtvyq+e8dW910BDioUfVQ0xHgIMYb8F98fqOsKG+oM37llemaOnVqsceGDh2qYcOGnfS9Ho9H0dHRRfddLpcCAgKUlpamsLCwv92WXzX/AAAAwLlm8ODBGjBgQLHHTjXrL0lut/ukrwUHBys7O5vmHwAAACjr/mqJz6mEhYVp9+7dxR7LzMxUYGBgiZ7PAb8AAADAOaJp06b6/vvvi+4nJSXJ6/WWaNZfovkHAAAAzhlxcXHKyMhQQkKCJGnGjBlq06aN3G53iZ7vKizNVQEAAAAAGLV69WqNGDFCoaGhys/P1zvvvKMGDRqU6Lk0/wAAAMA5JiUlRVu2bFGLFi0UERFR4ufR/AMAAACWYM0/AAAAYAmafwAlkpqaqm+//VZHjhwxHQUAAJwmmv8ztGvXLt10002Ki4vTxIkTZdMqqtTUVHXs2FFJSUmmozhm9erVuvrqq3XxxRfr5ptvLtXltM9lK1asUOfOnTV27Fh16NBBK1asMB3JUffcc48WL15sOoZjxo0bp5iYmKLbNddcYzqSoyZPnqwhQ4aYjuGIxYsXFxvrEzcbXu8JCQlq3769LrnkEt11113WfJYtWrRI3bp1U6tWrTR8+HAmdCxE838GvF6vhgwZosaNG2vRokVKTEy04g1Tko4cOaIhQ4YoOTnZdBTH/PLLLxo1apRGjBihtWvXqkaNGnryySdNx/K59PR0jRs3Tu+++64SEhL0j3/8Q5MnTzYdyzEffPCB1q9fbzqGo7Zu3aoZM2Zo48aN2rhxo5YsWWI6kmN27dql9957T6NGjTIdxRHdunUrGueNGzfq888/13nnnae4uDjT0Xzql19+0UsvvaRXX31VK1asUI0aNRQfH286ls99+eWXGj9+vOLj47V06VJ5PB4NHTrUdCw4jOb/DKxdu1Yej0fx8fGqXbu2hg8froULF5qO5Yjhw4era9eupmM4KjExUY888oi6du2qKlWqqF+/fvrxxx9Nx/K5zMxMjRo1Sg0bNpQkXXTRRUpLSzOcyhlHjx7VxIkTVbduXdNRHJOXl6ddu3apVatWqly5sipXrqyKFSuajuWIwsJCPf3007rzzjtVu3Zt03EcERQUVDTOlStXVkJCgjp37qxatWqZjuZT27ZtU2xsrBo3bqwaNWroxhtv1N69e03H8rmEhATdfPPNatu2rWrWrKnHHntM//nPf5Sammo6GhxE838GduzYodjYWIWEhEiSYmJirFkGMm7cON15552mYziqQ4cO6tevX9H9vXv3qk6dOgYTOaN69erq0aOHJCk3N1ezZs1S586dDadyxsSJE9WpUyc1b97cdBTH7Ny5U4WFherVq5eaNWume+65R/v37zcdyxELFizQjh07FB0drU8//VS5ubmmIzkqJydHc+bM0aBBg0xH8bn69evr66+/1rZt25SRkaH33ntPbdu2NR3L51JTU1WjRo2i+wEBx9vAcuXKmYoEA2j+z4DH41F0dHTRfZfLpYCAACtmRf19VujveL1ezZo1S/379zcdxTE7duxQ27Zt9cUXX1ixJOLrr7/WV199pUcffdR0FEclJiaqQYMGeuGFF7RixQoFBgbq6aefNh3L5zIzM/XSSy+pTp06OnjwoN566y3ddtttysnJMR3NMcuWLVNsbGyxzzV/Vb9+fXXp0kU33HCDWrVqpe+//16PP/646Vg+16hRI61Zs6bo+MTFixerWbNmqlSpkuFkcBLN/xlwu90KCgoq9lhwcLCys7MNJYJTXnrpJVWoUEF9+vQxHcUxMTExevvtt1W/fn2/Xxubk5OjZ555RmPGjLFmycsJPXr00IIFC9SsWTPVqlVLo0eP1hdffCGPx2M6mk+tWrVKx44d0+zZs/XAAw9o1qxZSk9PV0JCgulojpk3b5769u1rOoYjNm/erE8//VTvv/++vv32W3Xr1k333nuv35+04+6771Zubq5uvPFG9e3bV2+88YZuvfVW07HgMJr/MxAWFnbSUfKZmZkKDAw0lAhO+OKLLzRv3jy98MILVo21y+XSxRdfrAkTJmjNmjV+/ReuadOmqUmTJmrfvr3pKMZVrlxZBQUFOnTokOkoPnXw4EE1a9ZM4eHhko4vg4iJibHmDDD79u3TL7/8ojZt2piO4oiVK1fq+uuvV7NmzRQaGqqHH35YSUlJ2rFjh+loPhUeHq558+bppZdeUsOGDXXhhReqe/fupmPBYTT/Z6Bp06b6/vvvi+4nJSXJ6/UqLCzMYCr40q+//qqRI0dqzJgxql+/vuk4jvjqq680ceLEovtut1vSH2tF/dGyZcv0ySefqFWrVmrVqpWWL1+uf/zjHxozZozpaD733HPPaeXKlUX3t2zZooCAAFWvXt1gKt+rVq3aSUt89u/fX2x9tD/78MMP1b59e2smNPLz8/Xbb78V3c/MzFRWVpby8/MNpnJOZGSkVq1apREjRhS9p8MeHOFxBuLi4pSRkaGEhAT16tVLM2bMUJs2bdiR/FR2drYGDx6sTp066eqrr1ZmZqYkqUKFCnK5XIbT+c6FF16oBx54QBdccIGuvPJKvfTSS2rbtq1frxF97733lJeXV3T/+eefV2xsrG644QaDqZzRqFEjvfTSS6patary8vI0btw43XDDDUUnNvBX7du31/jx4zV37lx16NBBH3/8sbZv364XX3zRdDRHrFu3TjfeeKPpGI5p0aKFRo0apbffflvnn3++3n//fVWpUkUxMTGmozni3//+ty688EJ16tTJdBQY4Cr09wVuPrZ69WqNGDFCoaGhys/P1zvvvKMGDRqYjuWYmJgYrVmzxooDxFavXq0HHnjgpMdtqH/dunV67rnndPDgQbVr105jxoxRRESE6ViOeeKJJ3TppZda0xy98MILmjdvnkJDQ9WpUycNHz5cFSpUMB3L5zZv3qwJEyZo+/btqlKliuLj461ojrKzs9WqVSstXbpU9erVMx3HEYWFhXr11Ve1aNEiHT58WA0aNNC4cePUpEkT09F8Lj09Xddcc43eeOMNNWvWzHQcGEDzfxakpKRoy5YtatGihVUNEQAAAM4tNP8AAACAJfz3iD0AAAAAxdD8AwAAAJag+QcAAAAsQfMPAAAAWILmHwAAALAEzT8AnCVer/ekK4QWFhbK6/WW6ucUFhaqoKDgrGTKy8vThg0bTvp5e/bsUVZW1lnZBgDg3MGpPgHgNFxxxRUKDQ1VcHCwMjIydO211yolJUXbt29Xbm6uUlJSVLduXRUUFCg3N1crV67U/fffrz59+qhjx47asGGD/vz2W6VKlaILLL399tv64Ycfil1ddsqUKcrNzdVjjz2m/Px85eXlKTg4uFim3NxcBQQEFLvK+KJFi/Tss8/qjTfeUFhYmAIDA1WzZk1dd911uuOOO3T77bersLBQeXl5CgwM9PFvDQBgWjnTAQDgXLRu3TpJUlJSkm6++WbdcMMNRVf3/vzzz/Xmm2/q3//+d7Hn3HTTTXr88cf1yiuvaMiQIbr++uslSfv27VOtWrX04IMPKi0tTUFBQQoKCir23JCQkKKm/ptvvtGgQYMUGhpa9PW8vDwdO3ZMb731llq3bi1J+vXXXzVp0iTVqFFDAwcOVP369dW8eXPVrFlTv/32m1577TW9+OKLqlSpktq1a6d//vOfvvllAQDKDJp/ADgNOTk5mjZtmn755RcNGzasqPGXpEOHDqlOnTonPeeaa65Rs2bNFBkZqeDgYD311FPav3+/vv/+e/3nP//R+vXr9e2336pZs2ZFz/n999+VkZGhjIwMeb1e7d27V02bNtWPP/74P/Nt3rxZDz/8sPr3768BAwboiiuu0BtvvKFDhw5pwIABmj9/voKCgnT33Xfr448/VrlyfBwAgA14tweA07Rz50599913mjhxoqTjDfdTTz2l1NRU5efna/PmzZKk22+/XV26dNFnn32mXr16FT0/JSVFvXv31ujRoyVJbrdbAQHFD8X68ssvtWHDBq1evVo1atRQZmam+vTpo+bNm58yU35+vtxut2rXrq2HH364aHurVq3SqlWrNGnSJE2YMEENGzaUJD377LMnbRMA4L9o/gGglPLz8+VyufTKK68UrcUPCgpSXl6ezjvvPC1fvlw7d+5UrVq1NG3aNGVmZurIkSN65ZVXtHnz5qJmPzg4WJUqVfqf2+revbuuv/56rVy5Uu3bt9ddd92lW2+9VcnJySpXrpzy8vJUWFiowMBA5ebmKjQ0VOvWrVNERERR43/s2DHNmjVLK1eu1Lhx4zRt2jTt2LFDd911ly6//HJf/7oAAGUIzT8AlNK2bds0YsQISdKBAwe0du1a7d69W1OmTCn6nnvuuUdvvvmmJCkgIEAXXnih5s6dq7feeqtopr2wsFDly5f/2+199dVX8ng8Wrp0qbZv364lS5YUrf+fNm2asrKyNHLkyGLPKSws1M6dO/Xxxx9rwYIFatOmjZYsWaKIiAh16NBBc+bMUe/evXXVVVfpqquuUsOGDRUREXFWfj8AgLKL5h8ASqlp06b6+OOPlZiYqIceekivvPKKBg4cqBo1ahR9T05OjmrXrl3seZGRkbrrrrvkcrlUWFio1NRUhYeHS9L/PLXnv//9b9WuXVvXXHONEhIStGHDBmVlZalJkyZF37N3714dPXpUl1xyiSTprbfe0r/+9S9df/316t+/v6ZNm6ZPP/206B8eOTk5uvTSS3XxxRdrzpw5ysvL0/Tp0+Vyuc7WrwkAUAbR/APAGapatapee+01ZWZmSpIyMjIUFBSkChUqSFKxU3o+/PDDuuWWW5Sdna19+/YpMjJS0vHTdJ7q/P6ffvqptm7dqj59+kiSXnzxRTVq1EjXXHNN0bEG0vE1/fPnz9fy5csVEhKi2267TTfffLMqVaokr9er+++/X8nJyfr111912WWXSZLmzZunli1bqnv37r775QAAyhSO8gKAM1SxYkU1bNiw6GJeP/74o6Kjo4u+npOTI0nasWOHfvrpJ1199dX68MMPtW7dOjVu3Fht27bVuHHjlJ+fr9zc3GI/e/78+Ro/fnzROf0vu+wyff3116pSpYrat29f9H0DBgxQcHCwpk2bJkkKCgpSpUqV9Pvvv6t79+5F1x6YNGmSpOMHG48fP155eXk++70AAMoemn8AOEtiYmL0+OOPa/78+briiiv08ccf64477tAtt9wiSXr99dd1++23q1KlSgoODtZHH32k0NBQ9evXT1lZWbr22mv16KOPFvuZU6dO1VVXXVV0Py8vT6+88krR8qETAgMDNWTIEM2dO1cZGRlFj59//vlq0KCBVq1apdjYWO3du1cej0cfffSRrr766pOWJgEA/BvNPwCchoyMDB04cKDY1XQrVaqkVatWafv27br99tu1evVqPfLII8rNzdXatWv16aef6vbbb5ckTZw4UR06dNAdd9yhuLg4xcfHq3LlyoqKiir6S4GkovPv5+bmKj8/Xzk5Oerevbu6du2q1atX6z//+U/RQcNdu3bV8uXLi84gdGIJ0ZAhQ1S/fn253W69/fbbCgoK0sKFC9W7d29JYvYfACzCmn8AOA0zZ85UQkKCBg4cKOn4Up8nn3xSVapU0dy5cxUWFqbnn39ec+fOVd++fTVhwgQNHjxY5513nhISEvTVV19p0aJFkqRRo0bp8ccfV0ZGht566y3NmDHjpLP3eL1e5eXlKTQ0VIMHD5Ykvfnmm6pUqVLRmv1y5cqpWrVqRc9p0qSJKlSoUOwfKH/+eSNGjJDX65XX69XXX3+typUr++R3BQAoO1yFfz4SDQBwWgoKCrR582a1aNHipK8dOHBA1atXL7pfWFioQ4cOKSoq6qTvPXz4sLxer2rWrOnTvAAAO9H8AwAAAJZgzT8AAABgCZp/AAAAwBI0/wAAAIAlaP4BAAAAS9D8AwAAAJag+QcAAAAsQfMPAAAAWILmHwAAALDE/wdE5cO1aCmEIgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 Naive Bayes...\n",
      "训练集准确率: 0.4000\n",
      "测试集准确率: 0.2900\n",
      "交叉验证准确率: 0.2825 ± 0.0562\n",
      "训练时间: 0.01 秒\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "性能最好的模型: SVM\n",
      "测试集准确率: 0.3400\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "652938948c58a0f2"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
